ICED: 解釈可能な概念分解による概念レベルの機械学習忘却
ICED: Concept-level Machine Unlearning via Interpretable Concept Decomposition
視覚言語モデルにおいて、画像内の複雑に絡み合った概念を分解し、対象概念のみを選択的に忘却する概念レベルの機械学習忘却フレームワークを提案した。
著者: Shen Lin, Jing Lin, Junhao Dong, Piotr Koniusz, Li Xu
分類: cs.CV, cs.AI, cs.LG
原文アブストラクト
Machine unlearning in Vision-Language Models (VLMs) is typically performed at the image or instance level, making it difficult to precisely remove target knowledge without affecting unrelated semantics. This issue is especially pronounced since a single image often contains multiple entangled concepts, including both target concepts to be forgotten and contextual information that should be preserved. In this paper, we propose an interpretable concept-level unlearning framework for VLMs, which constructs a compact task-specific concept vocabulary from the forgetting set using a multimodal large language model. In addition to modality alignment, visual representations are decomposed into sparse, nonnegative combinations of semantic concepts, providing an explicit interface for fine-grained knowledge manipulation. Based on this decomposition, our method formulates unlearning as concept-level optimization, where target concepts are selectively suppressed while intra-instance non-target semantics and global cross-modal knowledge are preserved. Extensive experiments across both in-domain and out-of-domain forgetting settings demonstrate that our method enables more comprehensive target forgetting, better preserves non-target knowledge within the same image, and maintains competitive model utility compared with existing VLM unlearning methods.