拡散モデルのアンラーニングにおけるスパースオートエンコーダを用いた「見るが触れるな」アプローチ
Look But Don't Touch with Sparse Autoencoders for Unlearning in Diffusion Models
拡散モデルの概念消去において、スパースオートエンコーダの潜在空間への直接介入はアーティファクトを生じるため、検出のみに用いて対象領域のパッチを置換する手法を提案し、よりクリーンな消去を実現した。
著者: Enrico Cassano, Riccardo Renzulli, Rayyan Ahmed, Marco Grangetto, Stephan Alaniz
分類: cs.CV, cs.AI
原文アブストラクト
Sparse autoencoders (SAEs) have recently been proposed as interpretable tools for concept-level manipulation, under the assumption that isolated features can serve as controllable intervention points. In this work, we systematically evaluate this assumption in the context of object erasure and steering in diffusion models. We show that while SAEs reliably detect and localize semantic concepts within diffusion model activations, direct intervention in their latent space frequently induces out-of-distribution activations, resulting in severe visual artifacts. To disentangle detection from intervention, we use SAE activations purely as semantic detectors to identify image regions containing the target object, and replace those patch embeddings with the ones that do not contain it. This detection-based replacement preserves the diffusion model's activation statistics and produces significantly cleaner erasure results than latent steering. Our findings reveal a fundamental gap between concept detection and concept intervention in diffusion models: monosemantic or sparse features are not inherently suitable as control knobs for steering. These results position SAEs as powerful interpretability tools for analyzing generative models, but highlight important limitations when used for direct manipulation, such as unlearning.