title: Sparse Autoencoders for Scientifically Rigorous Interpretation of Vision
    Models
summary: 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.
paper_authors._id: 67ab7c4f84bf8aaa60cf1d8d
paper_authors.name: Samuel Stevens
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-12T09:16:50.887Z
paper_authors._id: 64df8592d27135dd568380b5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64df8592d27135dd568380b5/1-YkiTAkI11QBbZnVRjJu.jpeg
paper_authors.fullname: Samuel Stevens
paper_authors.type: user
paper_authors.user: samuelstevens
paper_authors._id: 67ab7c4f84bf8aaa60cf1d8e
paper_authors.name: Wei-Lun Chao
paper_authors._id: 67ab7c4f84bf8aaa60cf1d8f
paper_authors.name: Tanya Berger-Wolf
paper_authors._id: 67ab7c4f84bf8aaa60cf1d90
paper_authors.name: Yu Su
paper_title: Sparse Autoencoders for Scientifically Rigorous Interpretation of Vision
    Models
paper_summary: 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.
paper_ai_summary: A unified framework using sparse autoencoders enables identification and manipulation of interpretable visual features in vision models, facilitating hypothesis testing and understanding.
paper_ai_keywords: sparse autoencoders semantic abstractions model behavior human-interpretable visual features