5 citations · 5 across the 9 of their papers we have counts for
5 papers · 1 filter
Intern Lumina U2: A Multi-Codebook Diffusion Large Language Model for Omni-Visual Understanding and Image Generation
Qian Yu, Zongyang Qiu, Juncheng Yan +40
Unified multimodal generation covers discrete, continuous, and hybrid paradigms. Discrete approaches with single-codebook visual tokenizers align naturally with text modeling but h…
Confusion-Geometry Rebalancing for Long-Tailed Adversarial Training
Mengnan Zhao, Geyong Min, Lihe Zhang +2
Adversarial training under long tailed distributions suffers from a dual imbalance: the class imbalance skews the training objective toward head classes, and the adversarial inner…
CoreUnlearn: Rethinking Concept Unlearning through Disentangled Component-Level Erasure in Text-guided Diffusion Models
Mengnan Zhao, Lihe Zhang, Baocai Yin
Text guided diffusion models have revolutionized image synthesis but also raise ethical concerns, such as privacy violation and harmful content generation. To mitigate these issues…
Unveiling the Backdoor Mechanism Hidden Behind Catastrophic Overfitting in Fast Adversarial Training
Mengnan Zhao, Lihe Zhang, Tianhang Zheng +2
Fast Adversarial Training (FAT) has attracted significant attention due to its efficiency in enhancing neural network robustness against adversarial attacks. However, FAT is prone…
Mitigating Error Amplification in Fast Adversarial Training
Mengnan Zhao, Lihe Zhang, Bo Wang +3
Fast Adversarial Training (FAT) has proven effective in enhancing model robustness by encouraging networks to learn perturbation-invariant representations. However, FAT often suffe…