5 papers
AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss
Mingju Gao, Jingkai Zhou, Kun Gai +2
Fréchet distance has recently emerged as an effective distribution-level objective for generator post-training, complementing the conventional sample-level diffusion and flow-match…
SAID: Accelerating Diffusion-Based Language Models via Scaffold-Aware Iterative Decoding
Na Li, Chengda Wang, Mingju Gao +1
Diffusion large language models (DLLMs) enable non-autoregressive generation by iteratively denoising corrupted token sequences with bidirectional context. Despite their ability to…
SemBlock: Semantic Boundary Dynamic Blocks for Diffusion LLMs
Xinrui Song, Zhuoran Wang, Mingju Gao +1
Diffusion language models (DLMs) generate text through iterative denoising, and blockwise decoding improves their practicality by committing tokens in local blocks. However, existi…
ScoreAdv: Score-based Targeted Generation of Natural Adversarial Examples via Diffusion Models
Chihan Huang, Hao Tang
Despite the success of deep learning across various domains, it remains vulnerable to adversarial attacks. Although many existing adversarial attack methods achieve high success ra…
CtrlDiff: Boosting Large Diffusion Language Models with Dynamic Block Prediction and Controllable Generation
Chihan Huang, Hao Tang
Although autoregressive models have dominated language modeling in recent years, there has been a growing interest in exploring alternative paradigms to the conventional next-token…