
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."
He also recently led Encord's ML team to release the world's largest multimodal dataset, E-MM1.
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.
You can find him on:
Publications:
All articles by Frederik Hvilshøj

How to Clean Data for Computer Vision

Search Anything Model: Combining Vision and Natural Language in Search

Accelerating Robotics VLA Segmentation with SAM 3: Key Takeaways from the Masterclass

Multimodal Reinforcement Learning: Set Up Your Shop for RL Success

Introducing TTI-Eval: An Open-Source Library for Evaluating Text-to-Image Embedding Models

Introduction to Quality Metrics

What is One-Shot Learning in Computer Vision

The Advantages and Disadvantages of Synthetic Training Data

How to Use Low-Code and No-Code Tools for Computer Vision

5 Strategies To Build Successful Data Labeling Operations

Self-supervised Learning Explained

How to Use GPT-4o to Automate Captioning for VLA Models (and Build a Faster VLA Data Engine)

How We Built the World's Largest Multimodal Dataset

Segment Anything Model 3 (SAM 3): What to Expect from the Next Generation of Foundation Segmentation Models

3 Signs Your AI Evaluation Is Broken

Webinar Recap: Build Smarter VLMs, Faster - How to Bootstrap With Existing ML Solutions

Model Robustness: Building Reliable AI Models
Dec 06 2023
Data Management Solution: Key Features to Look For
Mar 05 2025
The Full Guide to Video Annotation for Computer Vision
Mar 19 2026
Data Visualization 101: Key Tools for Understanding Your Data
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Active Learning in Machine Learning: Guide & Strategies [2025]

The Full Guide to Automated Data Annotation

AI Agents in Action: A Guide to Building Agentic AI Workflows

What is a Digital Twin? Definition, Types & Examples

Visual Foundation Models (VFMs) Explained

Machine Learning Trends & Stats for 2024
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Best Datasets for Computer Vision [Industry breakdown]
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