Frederik Hvilshøj

Frederik Hvilshøj

Frederik is the Machine Learning Lead at Encord. He has an extensive computer vision and deep learning background and has completed a Ph.D. in Explainable Deep Learning and Generative Models at Aarhus University, and published research in Efficient Counterfactuals from Invertible Neural Networks and Back-propagation through Fréchet Inception Distance. Before his P.hD., Frederik studied for an M.Sc. in computer science while being a teaching assistant for "Introduction to databases" and "Pervasive computing and Software Architecture."

Frederik enjoys spending time with his two kids in his spare time and occasionally goes for long hikes around his hometown in the west of Denmark.

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All blogs by Frederik Hvilshøj

sampleImage_pros-cons-synthetic-training-data
Machine LearningData Operations
The Advantages and Disadvantages of Synthetic Training Data

7 min

sampleImage_synthetic-training-data-guide
Machine Learning
An Introduction to Synthetic Training Data

4 min

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Machine Learning
Self-supervised Learning Explained

sampleImage_one-shot-learning-guide
Machine Learning
What is One-Shot Learning in Computer Vision

3 min

sampleImage_automated-data-annotation-guide
Data Operations
The Full Guide to Automated Data Annotation

4 min

sampleImage_tti-eval-guide
Machine Learning
Introducing TTI-Eval: An Open-Source Library for Evaluating Text-to-Image Embedding Models

sampleImage_low-code-no-code-computer-vision-tools
Data Operations
How to Use Low-Code and No-Code Tools for Computer Vision

3 min

sampleImage_automate-labeling-with-llava-integration
Product
How to automate annotation with GPT-4 Vision’s rival, LLaVA

2 min

sampleImage_search-anything-model-introduction
Tutorials
Search Anything Model: Combining Vision and Natural Language in Search

sampleImage_quality-metric-guide
Data Quality
Introduction to Quality Metrics

3 min

sampleImage_video-annotation-guide
Data Operations
The Full Guide to Video Annotation for Computer Vision

15 min

sampleImage_build-data-labeling-ops
Data Operations
5 Strategies To Build Successful Data Labeling Operations

sampleImage_data-cleaning-computer-vision-guide
Data Quality
How to Clean Data for Computer Vision

12 min

sampleImage_evaluate-training-data-pipelines-guide
Data Operations
4 Questions to Ask When Evaluating Training Data Pipelines

5 min

sampleImage_generative-ai-in-manufacturing
Machine Learning
Real-World Use Cases of Generative AI in Manufacturing

sampleImage_visual-foundation-models-vfms-explained
Machine Learning
Visual Foundation Models (VFMs) Explained

sampleImage_active-learning-machine-learning-guide
Machine Learning
Active Learning in Machine Learning: Guide & Strategies [2024]

8 min

sampleImage_machine-learning-trends-statistics
Machine Learning
Machine Learning Trends & Stats for 2024

sampleImage_model-robustness-machine-learning-strategies
Data Quality
Model Robustness: Building Reliable AI Models 

8 min

sampleImage_best-datasets-for-machine-learning
Machine Learning
Best Datasets for Computer Vision [Industry breakdown]

10 min