Sparse Autoencoders for Scientifically Rigorous Interpretation of Vision
  Models
To truly understand vision models, we must not only interpret their learned
features but also validate these interpretations through controlled
experiments. Current approaches either provide interpretable features without
the ability to test their causal influence, or enable model editing without
interpretable controls. We present a unified framework using sparse
autoencoders (SAEs) that bridges this gap, allowing us to discover
human-interpretable visual features and precisely manipulate them to test
hypotheses about model behavior. By applying our method to state-of-the-art
vision models, we reveal key differences in the semantic abstractions learned
by models with different pre-training objectives. We then demonstrate the
practical usage of our framework through controlled interventions across
multiple vision tasks. We show that SAEs can reliably identify and manipulate
interpretable visual features without model re-training, providing a powerful
tool for understanding and controlling vision model behavior. We provide code,
demos and models on our project website: https://osu-nlp-group.github.io/SAE-V.
67ab7c4f84bf8aaa60cf1d8d
Samuel Stevens
claimed_verified
2025-02-12T09:16:50.887Z
64df8592d27135dd568380b5
https://cdn-avatars.huggingface.co/v1/production/uploads/64df8592d27135dd568380b5/1-YkiTAkI11QBbZnVRjJu.jpeg
Samuel Stevens
user
samuelstevens
67ab7c4f84bf8aaa60cf1d8e
Wei-Lun Chao
67ab7c4f84bf8aaa60cf1d8f
Tanya Berger-Wolf
67ab7c4f84bf8aaa60cf1d90
Yu Su
Sparse Autoencoders for Scientifically Rigorous Interpretation of Vision
  Models
To truly understand vision models, we must not only interpret their learned
features but also validate these interpretations through controlled
experiments. Current approaches either provide interpretable features without
the ability to test their causal influence, or enable model editing without
interpretable controls. We present a unified framework using sparse
autoencoders (SAEs) that bridges this gap, allowing us to discover
human-interpretable visual features and precisely manipulate them to test
hypotheses about model behavior. By applying our method to state-of-the-art
vision models, we reveal key differences in the semantic abstractions learned
by models with different pre-training objectives. We then demonstrate the
practical usage of our framework through controlled interventions across
multiple vision tasks. We show that SAEs can reliably identify and manipulate
interpretable visual features without model re-training, providing a powerful
tool for understanding and controlling vision model behavior. We provide code,
demos and models on our project website: https://osu-nlp-group.github.io/SAE-V.
A unified framework using sparse autoencoders enables identification and manipulation of interpretable visual features in vision models, facilitating hypothesis testing and understanding.
sparse autoencoders semantic abstractions model behavior human-interpretable visual features