MapRoute++: Surrogate-Guided Semantic Routing for Visual Concept Unlearning

2026-08-13

AI Summary

This work addresses the challenge of precisely removing specific visual concepts while preserving both unrelated and semantically proximate concepts in concept erasure tasks. Building upon the Stable Diffusion v1.4 framework, the authors propose a method that integrates a semantic routing mechanism with a proxy-guided strategy. By incorporating task-specific training objectives, enhanced concept representations, and a dynamic mapper selection scheme, the approach achieves fine-grained and high-fidelity concept forgetting. Evaluated on the official Genμ 2.0 Challenge benchmark, the method outperforms the current state-of-the-art baseline by an average of 12.1% in terms of the ERR metric, demonstrating consistent and significant improvements across all five concept categories.

Abstract

We present our submission to Task 3 of the Gen$μ$ 2.0 Challenge on visual concept unlearning. Building on MapRoute, we introduce task-specific training objectives, richer concept representations, and semantic routing for concept-specific mapper selection. Our approach improves robust concept removal while preserving unrelated and semantically adjacent concepts. On the official benchmark, evaluated using the Erasing-Retention-Robustness (ERR) metric on Stable Diffusion v1.4, our method outperforms the state-of-the-art baseline by 12.1\% on average across the five concept categories, achieving substantial gains.

Problem

• visual concept unlearning
• semantic routing
• concept removal
• Stable Diffusion
• unrelated concept preservation

Innovation

• semantic routing
• visual concept unlearning
• surrogate-guided
• concept-specific mapper
• ERR metric