Active Learning in Machine Learning: Guide & Strategies
What is Active Learning in Machine Learning?
Active Learning vs. Passive Learning
Advantages of Active Learning
Active Learning Query Strategies
Active Learning Informative Measures
Applications and Use Cases for Active Learning for Machine Learning
Tools to Use for Active Learning
Active Learning Key Takeaways
Active learning is a supervised machine learning approach that aims to optimize annotation using a few small training samples. One of the biggest challenges in building machine learning (ML) models is annotating large datasets. Active learning can help you overcome these challenges.
If you are building ML models, you must ensure that you have large enough volumes of annotated data and that your data contains valuable information from which your machine learning models can learn.
Unfortunately, data annotation can be a costly and time-consuming endeavor, especially when outsourcing this work to large teams of human annotators. Many teams don’t have the time, money, or manpower to label and review each piece of data in these vast datasets.
Fortunately, active learning pipelines and active learning algorithms and platforms can make this task much simpler, faster, and more accurate.
Active learning is a powerful technique that can help overcome these challenges by allowing a machine learning model to selectively query a human annotator for the most informative data points to label in image or video-based datasets. By iteratively selecting the most informative samples to label, active learning can help improve the accuracy of machine learning models while reducing the amount of labeled data required.
Active learning cycle in machine learning
In this comprehensive guide to active learning for machine learning, we will cover
- What is active learning in machine learning?
- Active learning vs. passive learning
- Advantages of active learning
- Active learning query strategies
- Active learning informative measures
- Applications and use cases for active learning for machine learning
- Tools to use for active learning
- Key takeaways
What is Active Learning in Machine Learning?
Active learning is a supervised machine learning approach that aims to optimize annotation using a few small training samples. During the training stage and even into the production stage, a supervised machine learning process involves a continuous feedback loop whereby annotators and data scientists provide more data points to keep improving the model's performance and accuracy.
A computer vision or machine learning model is trained on an initial, smaller subset of labeled data from a larger image or video-based dataset. Using these data points, it attempts to predict the rest of the unlabeled data based on what it has learned from its training.
ML engineers and data science teams evaluate how accurate the predictions are and, using a variety of acquisition functions, can quantify the impact of labeling a larger dataset volume or improving the accuracy of the labels generated, to improve the model's performance.
As this is a supervised learning process (compared to semi-supervised learning, or self-supervised), machine learning algorithms are attempting to outline what else it needs by expressing uncertainty in its predictions. In doing so, ML models are effectively asking human annotators to label a larger volume of the types of data it needs from the image or video samples provided.
When a machine learning model is generating low-accuracy results, it’s a clear sign that clear sign that the amount of data is insufficient. Annotation and data labeling teams supplying larger volumes of more accurately labeled data should improve the results a training model generates.
Here are some of the key concepts of active learning machine learning that you need to know.
The approach used to select the most informative samples from the pool of unlabeled data. Examples of query strategies include uncertainty sampling, diversity sampling, expected model change, and other approaches.
Selecting the best query strategy is very important as they decide which data points are informative and should be sent for labeling and used for further training. The efficiency of the active learning pipeline can be determined by how quickly the query strategy can select the most effective sample from the pool of unlabeled data. There are different types of query strategies, and they are discussed in more detail in later sections.
Human in the Loop
A human-in-the-loop annotator (HITL), quality assurance (QA) specialist, or ML engineer is responsible for labeling the most informative samples. The annotator can be an expert in the field or someone well-versed in the project's machine learning (ML) pipeline.
Annotators should have domain-specific knowledge (e.g., professional expertise and qualifications in healthcare). It’s important to have the ability to identify and label informative samples that can improve the machine learning model’s performance. The annotator should be able to identify patterns and important features in the data that can help the machine learning model make accurate predictions.
Additionally, the annotator should be able to evaluate the machine learning model's performance and adjust their annotation strategy accordingly.
Active Learning Loop
This is the iterative process of selecting examples, labeling them, updating the model, and selecting new examples to label.
Model uncertainty is the degree of uncertainty in the model's predictions, which can be used to guide the selection of informative examples.
The distribution of data in the pool of unlabeled examples can impact the effectiveness of different query strategies, and this, in turn, could negatively impact the active learningdeep learning pipeline.
Label efficiency is defined by the amount of labeled data required to achieve a certain level of performance, which can be reduced by using active learning.
Active Learning vs. Passive Learning
Passive learning and active learning are two different approaches to machine learning. In passive learning, the model is trained on a pre-defined labeled dataset, and the learning process is complete once the model is trained.
In contrast, in active learning, the informative data points are selected using query strategies instead of a pre-defined labeled dataset. These are then passed to be labeled by an annotator before being used to train the model. By iterating this process of using informative samples, we constantly work on improving the performance of a predictive model.
Here are some key differences between active and passive learning:
Differences between active and passive learning:
- Labeling: In active learning, a query strategy is used to determine the data to label and annotate, and the labels that need to be applied.
- Data selection: A query strategy is used to select data for training in active learning.
- Cost: Active learning requires human annotators, sometimes experts depending on the field (e.g., healthcare). Although costs can be kept under control with automated, AI-based labeling tools and active learning software.
- Performance: Active learning doesn't need as many labels due to the impact of informative samples. Passive learning needs more data, labels, and time spent training a model to achieve the same results.
- Adaptable: Active learning is more adaptable than passive learning especially with dynamic datasets.
Advantages of Active Learning
There are various advantages to using active learning for machine learning tasks, including:
Reduced Labeling Costs
Labeling large datasets is time-consuming and expensive. Active learning helps to reduce labeling costs by selecting the most informative samples that require labeling, including the use of techniques such as auto-segmentation.
The most informative samples are those that are expected to reduce the uncertainty of the model the most and thus provide the most significant improvements to the model's performance. By selecting the most informative samples, active learning can reduce the number of samples that need to be labeled, thereby reducing the labeling costs.
Active learning improves the accuracy of machine learning models by selecting the most informative samples for labeling. By focusing on the most informative samples, active learning can help to improve the model's performance.
Active learning algorithms are designed to select samples that are expected to reduce the uncertainty of the model the most. By focusing on these samples, active learning can significantly improve the accuracy of the model.
Active learning helps machine learning models to converge faster by selecting the most informative samples. The model can learn more quickly and converge faster by focusing on the most relevant samples.
Traditional machine learning models rely on random sampling or sampling based on specific criteria to select samples for training. However, these methods do not necessarily prioritize the most informative samples.
On the other hand, active learning algorithms are designed to identify the most informative samples and prioritize their inclusion in the training set, resulting in faster convergence.
The plot showing the active learning algorithm (blue) converges faster than the general machine learning algorithm (red).
Active learning helps machine learning models to generalize better to new data by selecting the most diverse samples for labeling. Active learning Python formulas or deep learning networks improve a model's reinforcement learning capabilities. The model can learn to recognize patterns and generalize better to new data by focusing on diverse samples, including outliers, even when there’s a large amount of data.
Diverse samples cover a broad range of the feature space, ensuring that the model learns to recognize patterns relevant to a wide range of scenarios. Active learning can help the model generalize better to new data by including diverse samples in the training set.
Robustness to Noise
Another way active learning works is to improve the robustness of machine learning models to noise in the data. By selecting the most informative samples, active learning algorithms are trained on the samples that best represent the entire dataset. Hence, the models trained on these samples will perform well on the best data points and the outliers.
Having discovered the benefits of active learning, we will investigate the query techniques involved so we can apply them to our existing machine learning model.
Active Learning Query Strategies
As we discussed above, active learning improves the efficiency of the training process by selecting the most valuable data points from an unlabeled dataset. This step of selecting the data points, or query strategy, can be categorized into three methods.
Stream-based Selective Sampling
Stream-based selective sampling is a query strategy used in active learning when the data is generated in a continuous stream, such as in online or real-time data analysis.
In this, a model is trained incrementally on a stream of data, and at each step, the model selects the most informative samples for labeling to improve its performance. The model selects the most informative sample using a sampling strategy.
The sampling strategy measures the informativeness of the samples and determines which samples the model should request labels for to improve its performance. For example, uncertainty sampling selects the samples the model is most uncertain about, while diversity sampling selects the samples most dissimilar to the samples already seen.
Stream-based sampling is particularly useful in applications where data is continuously generated, like processing real-time video data. Here, it may not be feasible to wait for a batch of data to accumulate before selecting samples for labeling. Instead, the model must continuously adapt to new data and select the most informative samples as they arrive.
Stream-based selective sampling
This approach has several advantages and disadvantages, which should be considered before selecting this query strategy.
Advantages of Stream-based Selective Sampling
- Reduced labeling cost: Stream-based selective sampling reduces the cost of labeling by allowing the algorithm to selectively label only the most informative samples in the data stream. This can be especially useful when the cost of labeling is high and labeling all incoming data is not feasible.
- Adaptability to changing data distribution: This strategy is highly adaptive to changes in the data distribution. As new data constantly arrives in the stream, the model can quickly adapt to changes and adjust its predictions accordingly.
- Improved scalability: Stream-based selective sampling allows for improved scalability since it can handle large amounts of incoming data without storing all the data.
Disadvantages of Stream-based Selective Sampling
- Potential for bias: Stream-based selective sampling can introduce bias into the model if it only labels certain data types. This can lead to a model that is only optimized for certain data types and may not generalize well to new data.
- Difficulty in sample selection: This sampling strategy requires careful selection of which samples to label, as the algorithm only labels a small subset of the incoming data. Selection of the wrong samples to label can result in a less accurate model than a model trained with a randomly selected labeled dataset.
- Dependency on the streaming platform: Stream-based selective sampling depends on the streaming platform and its capabilities. This can limit the approach's applicability to certain data streams or platforms.
Pool-based sampling is a popular method used in active learning to select the most informative examples for labeling. In this approach, a pool of unlabeled data is created, and the model selects the most informative examples from this pool to be labeled by an expert or a human annotator.
The newly labeled examples are then used to retrain the model, and the process is repeated until the desired level of model performance is achieved. Pool-based sampling can be further categorized into uncertainty sampling, query-by-committee, and density-weighted sampling. We will discuss these in the next section. For now, let’s look at the advantages and disadvantages of pool-based sampling.
Pool-based sampling method
Advantages of Pool-based sampling
- Reduced labeling cost: Pool-based sampling reduces the overall labeling cost compared to traditional supervised learning methods since it only requires labeling the most informative sample. This can lead to significant cost savings, especially when dealing with large datasets.
- Efficient use of expert time: Since the expert is only required to label the most informative samples, this strategy allows for efficient use of expert time, saving time and resources.
- Improves model performance: The selected samples are more likely to be informative and representative of the data, so pool-based sampling can improve the model's accuracy.
Disadvantages of Pool-based sampling
- Selection of the pool of unlabeled data: The quality of the selected data affects the performance of the model, so careful selection of the pool of unlabeled data is essential. This can be challenging, especially for large and complex datasets.
- Quality of the selection method: The quality of the selection method used to choose the most informative sample can affect the model’s accuracy. The model's accuracy may suffer if the selection method is not appropriate for the data or is poorly designed.
- Not suitable for all data types: Pool-based sampling may not be suitable for all types of data, such as unstructured data or noisy data. In these cases, other active learning approaches may be more appropriate.
Query Synthesis Methods
Query synthesis methods are a group of active learning strategies that generate new samples for labeling by synthesizing them from the existing labeled data.
The methods are useful when your labeled dataset is small, and the cost of obtaining new labeled samples is high.
One approach to query synthesis is by perturbing the existing labeled data, for example, by adding noise or flipping labels.
Another approach is to generate new samples by interpolating or extrapolating from existing samples in the labeled dataset, and the model is retrained. Generative Adversarial Networks (GANs) and Visual Foundation Models (VFMs) are two popular methods for generating synthetic data samples.
These data samples are adapted to the current model. The annotator labels these synthetic samples, which are added to the training dataset. The model learns from these synthetic samples generated by the GANs.
Query synthesis method with unlabeled data
Query synthesis method with labeled data
Advantages of Query Synthesis
- Increased data diversity: Query synthesis methods can help increase the diversity of the training data, which can improve the model's performance by reducing overfitting and improving generalization.
- Reduced labeling cost: Like the other query strategies discussed above, query synthesis methods also reduce the need for manual labeling and hence lower the overall labeling cost. These methods achieve this by generating new unlabeled samples.
- Improved model performance: The synthetic samples generated using query synthesis methods can be more representative of the data, improving the model’s performance by providing it with more informative and diverse training data.
Disadvantages of Query Synthesis
- Computational cost: Query synthesis methods can be computationally expensive, especially for complex data types like images or videos. Generating synthetic examples can require significant computational resources, limiting their applicability in practice.
- Limited quality of the synthetic data: The quality of the synthetic data generated using query synthesis methods depends on the selection of the method and the parameters used. Poor selection of the method or parameters can lead to the generation of synthetic examples that are not representative of the data, which can negatively impact the model's performance.
- Overfitting: Generating too many synthetic examples can lead to overfitting, where the model learns to classify the synthetic examples instead of the actual data. This can reduce the model's performance on new, unseen data.
Flow chart showing the three sampling methods
Active learning query strategies typically involve evaluating the informativeness of the unlabeled samples, which can either be generated synthetically or sampled from a given distribution. In general, these strategies can be categorized into different query strategy frameworks, each with a unique process for selecting the most informative sample.
An overview of these query strategy frameworks can help to understand the active learning process better.
By identifying the framework that best fits a particular problem, machine learning researchers and practitioners can make informed decisions about which query strategy to use to maximize the effectiveness of the active learning approach.
Active Learning Informative Measures
Now let's take a closer look at a series of informative measures you can take, such as uncertainty sampling and query-by committee, and others.
Uncertainty sampling is a query strategy that selects samples that are expected to reduce the uncertainty of the model the most. The uncertainty of the model is typically measured using a measure of uncertainty, such as entropy or margin-based uncertainty. Samples with high uncertainty are selected for labeling, as they are expected to provide the most significant improvements to the model's performance.
Query-By Committee Sampling
Query-by committee is a query strategy that involves training multiple models on different subsets of the labeled dataset and selecting samples based on the disagreement among the models.
This strategy is useful when the model tends to make errors on specific samples or classes. By selecting samples on which the committee of models disagrees, the model can learn to recognize sample patterns and improve its performance in those classes.
Diversity Weighted Methods
Diversity-weighted methods select examples for labeling based on their diversity in the current training set. It involves ranking the pool of unlabeled examples based on a diversity measure, such as the dissimilarity between examples or the uncertainty of the model's predictions.
The most diverse examples are then labeled to improve the model's generalization performance by providing it with informative and representative training data.
Learning curves of the model training with diversity. The dashed line represents the performance of the backbone classifier trained on the entire dataset.
Expected Model-change-based Sampling
Expected model-change-based sampling is an active learning method that selects examples for labeling based on the expected change in the model's predictions. This approach aims to select examples likely to cause the most significant changes in the model's predictions when labeled to improve the model's performance on new, unseen data.
In expected model-change-based sampling, the unlabeled examples are first ranked based on estimating the expected change in the model's predictions when each example is labeled.
This estimation can be based on various measures such as expected model output variance, expected gradient magnitude, or by measuring the Euclidean distance between the current model parameters and the expected model parameters after labeling.
Using this approach, the examples that are expected to cause the most significant changes in the model's predictions are then selected for labeling, with the idea that these examples will provide the most informative training data for the model. These samples are then added to the training data to update the model.
Expected Error Reduction
Expected error reduction is an active learning method that selects examples for labeling based on the expected reduction in the model's prediction error. The idea behind this approach is to select examples likely to reduce the model's prediction error the most when labeled, to improve the model's performance on new, unseen data.
In expected error reduction, the unlabeled examples are first ranked based on estimating the expected reduction in the model's prediction error when each example is labeled. This estimation can be based on various measures, such as the distance to the decision boundary, the margin between the predicted labels, or the expected entropy reduction.
The examples expected to reduce the model's prediction error are then selected for labeling, with the idea that these examples will provide the most informative training data for the model.
Having comprehended the concept of active learning and its implementation on different data types, let us explore its uses. This will aid us in recognizing the importance of including active learning in our machine learning system.
Applications and Use Cases for Active Learning for Machine Learning
As active learning can generate optimal results even with a few labeled examples, it has several practical applications in all areas of machine learning. It frequently replaces or supports conventional supervised learning, saving ML teams significant resources.
Active learning has numerous applications in computer vision, where it can be used to reduce the amount of labeled data needed to train models for a variety of tasks.
Active learning can be used to reduce the amount of labeled data needed to train image classification models, which can be particularly useful in applications where the number of classes is large and the data is imbalanced.
For example, Cost-Effective Active Learning (CEAL) uses active learning to build a classifier with optimal feature representation. This approach advances the existing active learning methods in two aspects.
Firstly, it incorporates deep convolutional neural networks (CNNs) into active learning so that the classifier and the features are simultaneously updated with annotated informative samples.
Secondly, it uses a cost-effective sample selection strategy to improve classification performance with fewer manual annotations.
Image classification flowchart from Cost-Effective Active Learning for Deep Image Classification
Active learning can reduce the amount of labeled data needed to train semantic segmentation models, which can be particularly useful in applications where high-resolution labeling data is difficult to obtain.
For example, instead of using the whole labeled dataset, we can select images using uncertainty sampling, as proposed in ViewAL.
The authors introduce a measure of uncertainty based on inconsistencies in model predictions across different viewpoints, which encourages the model to perform well regardless of the viewpoint of the objects being observed.
They also propose a method for computing uncertainty on a superpixel level, which lowers annotation costs by exploiting localized signals in the segmentation task.
By combining these approaches, the authors can efficiently select highly informative samples for improving the network's performance.
Active learning can be used to reduce the amount of labeled data needed to train object detection models. This can be particularly useful in applications where the number of object classes is large and the data is imbalanced, as active learning selects the most informative sample, which is the best representation of the dataset.
An imbalanced dataset doesn’t have enough data points for each class, causing an imbalance. But, the informative samples selected by query strategies represent the whole dataset and help in training a model which is robust to data imbalance.
For example, the Multiple Instance Active Object Detection model or MI-AOD uses active learning for the task of object detection. This algorithm selects the most informative images for detector training by observing instance-level uncertainty. It defines an instance uncertainty learning module, which leverages the discrepancy of two adversarial instance classifiers trained on the labeled set to predict the instance uncertainty of the unlabeled set.
Comparison of active object detection methods. (a) Conventional methods compute image certainty by averaging instance uncertainties, ignoring interference from a large number of background instances. (b) MI-AOD leverages uncertainty re-weighting using multiple learning to filter out interfering instances. It bridges the gap between instance uncertainty and image uncertainty.
Natural Language Processing (NLP)
Active learning has numerous applications in NLP, which can be used to reduce the amount of labeled data needed to train models for various tasks.
For example, in named entity recognition (NER), active learning can reduce the amount of labeled data needed to train NER models, which involves identifying named entities (such as people, organizations, and locations) in text.
It can be used to reduce the amount of labeled data needed to train machine translation models, which involves translating text from one language to another. One example of this is in the Curriculum Learning Framework.
This framework consists of a principled way of deciding which training samples the model uses at different times during training. This is based on a sample's estimated difficulty and the model's current competence.
In contrast to the usual method of uniformly choosing training instances, filtering training samples prevents the model from being stuck in bad local optima, which speeds convergence and improves the solution.
Audio processing uses labeled data to train models for tasks like speech recognition, speaker identification, music genre classification, or acoustic event detection.
Active learning can be valuable in reducing the labeled data needed for these audio-processing tasks.
For example, this paper uses active learning for sound event detection (SED). The proposed system analyzes an initially unlabeled audio dataset, from which it selects sound segments for manual annotation.
The proposed system analyzes an initially unlabeled audio dataset, querying for weak labels on selected sound segments from the dataset. A change point detection method is used
to generate variable-length audio segments. The segments are selected and presented to an annotator based on the principle of mismatch-first farthest-traversal.
During ML model training, full recordings are used as input to preserve the long-term context for annotated segments.
During the training of SED models, recordings are used as training inputs, preserving the long-term context for annotated segments. With this, the annotation effort can be greatly reduced on the dataset where target sound events are rare.
On the dataset with rare events, more than 90% of the labeling budget can be saved by using the proposed system, with respect to a system that uses random sampling and annotated segments only for model learning. By annotating only 2% of the training data, the achieved SED performance is similar to annotating all the training data.
There are many strategies and methods you should experiment with before adding the right active learning pipeline to your machine learning project. But there are active learning platforms that provide support, so you can start immediately! Let’s have a look at these platforms in detail.
Tools to Use for Active Learning
Here are some of the most popular tools for active learning:
Encord Active (GitHub repo can be found here) is an open-source active learning toolkit that helps automatically find and fix dataset errors and biases, explain and improve model performance, and intelligently curate your data. It's the best option for teams that:
- Are looking for a solution that integrates tightly with state-of-the-art automated annotation and workflow tools to enable real-time active learning workflows;
- Are already deploying or aiming to deploy discriminative or generative models in production environments soon;
- Are building advanced artificial intelligence applications requiring a diverse set of pre-defined and custom parametrizations (or "Quality Metrics") added onto their training data and models.
Encord Active was designed to compute, store, inspect, manipulate, and utilize quality metrics for various functionality. It hosts a library of these quality metrics and, importantly, allows you to customize by writing your metrics to calculate/compute quality metrics across your dataset.
- Open-source and deployed version
- Specialized in computer vision with support for a broad set of visual modalities
- Native integration with standard annotation tools and Encord Annotate, a state-of-the-art AI-assisted labeling and workflow tooling platform
- Advanced data curation features
- Support for all annotation types - bounding box, polygon, polyline, instance segmentation, keypoints, classification, and more
- Supports evaluating your training data based on a trained model and imported model predictions with acquisition functions such as entropy, least confidence, margin, and variance with pre-built implementations
- Visual interface with data distribution, image similarity, correlation, and image embeddings exploration functionality
- Allow you to systematically evaluate and rank the quality of your data and labels against pre-defined or custom metrics, such as brightness, image singularity, annotation duplicates, closeness to image borders, occlusions in video or image sequences, frame object density, and many more.
- Advanced Python SDK and API access (+ easy export into JSON and COCO formats)
- Teams looking for an integrated and secure commercial-grade enterprise platform encompassing both annotation tooling and workflow management alongside an expansive active learning feature set and -
- Data science, machine learning, and data operations teams who are seeking to use pre-defined or add custom metrics for parametrizing their data, labels, and models.
- Encord Active is open-source under the Apache-2.0 license and is available as a hosted and fully integrated version of the Encord platform.
Lightly is a platform that combines active learning with data curation, annotation, and management, enabling users to create high-quality training datasets with minimal effort. Its AI-powered active learning techniques help users prioritize the most relevant and informative data points to label, leading to improved model performance.
- Web interface for data curation and visualization
- Supports image, video, and point cloud data for computer vision tasks
- Supports active learning strategies such as uncertainty sampling, core-set, and representation-based approaches
- Integrations with popular annotation tools and platforms
- Python SDK for seamless integration into existing workflows
- Data scientists and machine learning engineers who want an intuitive, end-to-end solution for active learning, data curation, and annotation tasks
- Lightly offers a free tier with basic features and limited usage. Paid plans with additional features and scalability start at $280 per month.
Cleanlab is a popular open-source tool focused on data-centric AI. It provides algorithms and interfaces to help companies across a broad set of industries improve the quality of their datasets and diagnose and fix various issues. Cleanlab offers three main products: Cleanlab Research, Cleanlab Open-source, and Cleanlab Studio.
- Open-source through Cleanlab Opens-source and deployed version, Cleanlab Studio
- Supports images, text, and tabular data for classification tasks
- Scoring and tracking features to monitor data quality over time continuously
- Visual playground with a sandbox implementation
- Individual researchers and smaller teams looking to solve simple classification tasks and find outliers across different data modalities.
- Cleanlab Open-source is open-sourced under the GNU General Public License v3.0 and is available as a hosted version in Cleanlab Studio.
Voxel51 is the company behind FiftyOne, an open-source toolkit designed to enhance computer vision workflows by improving dataset quality and providing valuable insights into deep learning model performance. FiftyOne empowers teams to collaborate securely on datasets in the cloud, streamlining the process of creating, curating, and managing high-quality data for machine learning models.
- Effortlessly explore, search, and slice datasets to find samples and labels that meet specific criteria.
- Leverage tight integrations with public datasets or create custom datasets to train models on relevant, high-quality data.
- Optimize model performance by using FiftyOne to identify, visualize, and correct failure modes.
- Automate the process of finding and correcting label errors to curate higher quality datasets efficiently.
- Utilize the FiftyOne Brain for scalable identification of edge cases, mining new samples for training, and more.
- Build data-centric pipelines with FiftyOne and PyTorch to surface high-quality data and develop production-ready models more efficiently.
- Data scientists and machine learning engineers working on computer vision projects who seek an efficient and powerful solution for data visualization, curation, and model improvement, with an emphasis on data quality and building streamlined workflows allowing for rapid iteration.
Active Learning Key Takeaways
- Active learning is an important concept in machine learning that can significantly reduce the amount of labeled data required for training a model while achieving better performance.
- By selecting informative examples from a pool of unlabeled data to be labeled by an annotator, active learning can make the most efficient use of resources while achieving high performance.
- The iterative process of selecting examples, labeling them, updating the model, and selecting new examples to label is an effective way to build robust models that can adapt to dynamic datasets.
- Additionally, active learning can be used in a wide range of applications, including image classification, natural language processing, and recommendation systems.
- Incorporating active learning into the machine learning workflow can result in significant benefits, making it an essential technique for data scientists and machine learning engineers.
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