arXiv AI

Efficient bias mitigation in T2I diffusion models using Concept Graphs

arXiv:2607. 03397v1 Announce Type: new Abstract: Text-to-Image diffusion models often propagate harmful bias inherited from the training data.

arXiv Machine Learning

Jun 9

A Unifying Framework for Concept-Based Representational Similarity

arXiv:2606. 09653v1 Announce Type: new Abstract: Learned representations across models and modalities often exhibit striking structural similarities, suggesting shared underlying concept decompositions.

By Gr\'egoire Dhimo\"ila, Victor Boutin, Agustin Martin Picard, Thomas Fel, Thomas Serre

benchmarks safety

arXiv AI

Aug 11

Explaining, Verifying, and Aligning Semantic Hierarchies in Vision-Language Model Embeddings

arXiv:2603. 26798v2 Announce Type: replace-cross Abstract: Vision-language model (VLM) encoders such as CLIP enable strong retrieval and zero-shot classification in a shared image-text embedding space, yet the semantic organization of this space is rarely inspected.

By Gesina Schwalbe, Mert Keser, Moritz Bayerkuhnlein, Edgar Heinert, Annika M\"utze, Marvin Keller, Sparsh Tiwari, Georgii Mikriukov, Diedrich Wolter, Jae Hee Lee, Matthias Rottmann

llms rag multimodal safety

arXiv AI

Jul 2

The Illusion of High Utility in Safety Alignment of Text-to-Image Diffusion Models

arXiv:2607. 00402v1 Announce Type: cross Abstract: Safety alignment of text-to-image (T2I) diffusion models aims to suppress harmful generations while preserving utility on benign prompts.

By Adeel Yousaf, Soumik Ghosh, James Beetham, Amrit Singh Bedi, Mubarak Shah

rag diffusion nlp benchmarks safety

arXiv AI

Jul 8

TILDE: TILt-based Distributional Erasure for Concept Unlearning

arXiv:2607. 06432v1 Announce Type: cross Abstract: Concept unlearning in text-to-image diffusion models is critical for safe and practical deployment: with rising privacy concerns, copyright disputes, trademark constraints, and safety regulations, deployed systems must be able to suppress unwanted concepts after training.

By Naveen George, Naoki Murata, Yuhta Takida, Konda Reddy Mopuri, Yuki Mitsufuji

diffusion safety

arXiv Machine Learning

Jul 10

AutoAnchor: Stable Diffusion Unlearning Using Cross-Attention as a Manifold Surrogate

arXiv:2607. 08337v1 Announce Type: new Abstract: Diffusion unlearning is essential for mitigating the generation of harmful or copyrighted content in text-to-image models.

By Siyuan Wen, Jiahao Zeng, Ningning Ding

diffusion benchmarks

Hugging Face Trending Papers

Jul 9

AutoAnchor: Stable Diffusion Unlearning Using Cross-Attention as a Manifold Surrogate

Diffusion unlearning is essential for mitigating the generation of harmful or copyrighted content in text-to-image models. Current diffusion unlearning techniques determine the model update direction by either using alternatives of the target concept as an anchor or using empty prompts.

diffusion benchmarks

arXiv AI

Jun 3

WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation

arXiv:2503. 07265v4 Announce Type: replace-cross Abstract: Text-to-Image (T2I) models are capable of generating high-quality artistic creations and visual content.

By Yuwei Niu, Munan Ning, Mengren Zheng, Weiyang Jin, Bin Lin, Peng Jin, Jiaqi Liao, Chaoran Feng, Fanqing Meng, Kunpeng Ning, Bin Zhu, Li Yuan

diffusion multimodal benchmarks safety

arXiv AI

Jun 16

SACE: Concept Erasure at the Semantic Singularity in Visual Autoregressive Models

arXiv:2606. 15819v1 Announce Type: cross Abstract: The rapid progress of visual autoregressive (VAR) models has unlocked a transformative frontier for high-fidelity text-to-image synthesis, while heightening concerns over the safety alignment of generated content.

By Siya Yang, Nanxiang Jiang, Zhaoxin Fan, Yunfeng Diao

diffusion safety

arXiv AI

Jun 10

Improving Text-Instance Alignment Of Foreground Conditioned Out-Painting Via Customized Concept Embedding

arXiv:2606. 10892v1 Announce Type: cross Abstract: To showcase products, merchants often incur substantial costs creating high-quality display images.

By Yihao Zhao, Xuan Han, Bin He, Mingyu You

rag diffusion safety

arXiv AI

Jun 10

ERAlign: Energy-based Representation Alignment of GNNs and LLMs on Text-attributed Graphs

arXiv:2606. 10461v1 Announce Type: cross Abstract: Text-attributed Graphs (TAGs) incorporate textual node attributes with graph structures to describe rich relational semantics.

By Xianlin Zeng, Fan Xia, Xiangyu Chen

llms rag diffusion benchmarks safety

arXiv Machine Learning

Aug 12

Clarity: The Flexibility-Interpretability Trade-Off in Sparsity-aware Concept Bottleneck Models

arXiv:2601. 21944v3 Announce Type: replace Abstract: The widespread adoption of deep learning models in computer vision has intensified concerns about interpretability.

By Konstantinos P. Panousis, Diego Marcos

computer-vision efficiency multimodal safety

arXiv Machine Learning

Aug 4

Semantic Alignment of AI Models: Concept Collapse, Checkpoint Dynamics, and Cross-Lingual Transfer

arXiv:2608. 01585v1 Announce Type: cross Abstract: Language model benchmarking is a difficult task.

By Tyler Ashoff, Jordan Rodu

llms rag multimodal benchmarks safety

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