Author

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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Frederik Hvilshøj

All blogs by Frederik Hvilshøj

sampleImage_webinar-recap-build-smarter-vlms-faster
Machine LearningLearn
Webinar Recap: Build Smarter VLMs, Faster - How to Bootstrap With Existing ML Solutions
sampleImage_low-code-no-code-computer-vision-tools
Data Operations
How to Use Low-Code and No-Code Tools for Computer Vision

3 m

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

3 m

sampleImage_build-data-labeling-ops
Data Operations
5 Strategies To Build Successful Data Labeling Operations
sampleImage_self-supervised-learning
Machine Learning
Self-supervised Learning Explained
sampleImage_quality-metric-guide
Data Operations
Introduction to Quality Metrics

3 m

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

12 m

sampleImage_search-anything-model-introduction
Computer VisionText
Search Anything Model: Combining Vision and Natural Language in Search
sampleImage_tti-eval-guide
Machine LearningModel Evaluation
Introducing TTI-Eval: An Open-Source Library for Evaluating Text-to-Image Embedding Models
sampleImage_pros-cons-synthetic-training-data
Machine LearningData Operations
The Advantages and Disadvantages of Synthetic Training Data

7 m

sampleImage_ai-agents-guide-to-agentic-ai
Machine LearningAgents
AI Agents in Action: A Guide to Building Agentic AI Workflows
sampleImage_digital-twin
Computer VisionHealthcare
What is a Digital Twin? Definition, Types & Examples
sampleImage_automated-data-annotation-guide
Data Operations
The Full Guide to Automated Data Annotation

4 m

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

8 m

sampleImage_data-management-solution
Data OperationsData Quality
Data Management Solution: Key Features to Look For
sampleImage_active-learning-machine-learning-guide
Machine Learning
Active Learning in Machine Learning: Guide & Strategies [2025]

8 m

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

15 m

sampleImage_data-visualization-key-tools
Data Operations
Data Visualization 101: Key Tools for Understanding Your Data
sampleImage_visual-foundation-models-vfms-explained
Machine Learning
Visual Foundation Models (VFMs) Explained
sampleImage_machine-learning-trends-statistics
Machine Learning
Machine Learning Trends & Stats for 2024
sampleImage_testing-blog-compare-chart
Best Datasets for Computer Vision [Industry breakdown]

10 m

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

10 m