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Separable Multi-Concept Erasure from Diffusion Models
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Large-scale diffusion models, known for their impressive image generation capabilities, have raised concerns among researchers regarding social impacts, such as the imitation of copyrighted artistic styles. In response, existing approaches turn to machine unlearning techniques to eliminate unsafe concepts from pre-trained models. However, these methods compromise the generative performance and neglect the coupling among multi-concept erasures, as well as the concept restoration problem. To address these issues, we propose a Separable Multi-concept Eraser (SepME), which mainly includes two parts: the generation of concept-irrelevant representations and the weight decoupling. The former aims to avoid unlearning substantial information that is irrelevant to forgotten concepts. The latter separates optimizable model weights, making each weight increment correspond to a specific concept erasure without affecting generative performance on other concepts. Specifically, the weight increment for erasing a specified concept is formulated as a linear combination of solutions calculated based on other known undesirable concepts. Extensive experiments indicate the efficacy of our approach in eliminating concepts, preserving model performance, and offering flexibility in the erasure or recovery of various concepts.
Forward citations
Cited by 8 Pith papers
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ACE: Anti-Editing Concept Erasure in Text-to-Image Models
ACE trains a LoRA adapter on both conditional and unconditional noise predictions so that erased concepts are suppressed during both generation and text-guided editing.
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AdvAnchor: Enhancing Diffusion Model Unlearning with Adversarial Anchors
AdvAnchor generates adversarial anchors, embeddings perturbed to be dissimilar from the target concept, and fine-tunes the model toward them, improving the erasure-preservation trade-off in diffusion model unlearning.
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Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models
A bilevel training procedure that simultaneously restores a pruned diffusion model's quality and suppresses targeted concepts beats sequential fine-tuning followed by unlearning.
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Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
A system-first taxonomy and literature synthesis of multimodal unlearning across vision, language, video, and audio, with datasets, benchmarks, metrics, applications, and open challenges.
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BalDRO: A Distributionally Robust Optimization based Framework for Large Language Model Unlearning
BalDRO makes LLM unlearning more balanced by updating against a worst-case-weighted forget distribution, improving forget quality on TOFU/MUSE at stable utility.
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SecureT2I: No More Unauthorized Manipulation on AI Generated Images from Prompts
A diffusion editing model is fine-tuned with a blur target for forbidden images and the original output for permitted images, claiming selective suppression of unauthorized edits.
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Erasing Concepts, Steering Generations: A Comprehensive Survey of Concept Suppression
This survey classifies concept erasure methods for text-to-image diffusion models along intervention level, optimization strategy, and semantic scope, and reviews the datasets, metrics, and benchmarks used to evaluate them.
- Yuan: Yielding Unblemished Aesthetics Through A Unified Network for Visual Imperfections Removal in Generated Images
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