In Search of Forgotten Domain Generalization | Robust Machine Learning – Max Planck Institute for Intelligent Systems

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Research Overview

Comparative Vision Science

Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity?

In Search of Forgotten Domain Generalization

Partial Success in Closing the Gap between Human and Machine Vision

Foundations of Machine Learning

Cross-Entropy Is All You Need To Invert the Data Generating Process

Contrastive Learning Inverts the Data Generating Process

Interaction Asymmetry: A General Principle for Learning Composable Abstractions

Mechanistic Interpretability

Measuring Per-Unit Interpretability at Scale Without Humans

Exemplary Natural Images explain CNN Activations better than State-Of-The-Art Feature Visualizations

How Well do Feature Visualizations Support Causal Understanding of CNN Activations?

Robust Machine Learning

In Search of Forgotten Domain Generalization

Forgotten domain — To meaningfully study OOD generalization in web-scale datasets, we split LAION into natural images, sketches, and ambiguous images.

Here, we create OOD-specific datasets from LAION to reveal that much of CLIP’s perceived generalization is driven by in-domain examples, showing that challenges from the ImageNet era persist and offering a framework for meaningful assessment and optimization of OOD robustness [

].