The increasing reliance on large-scale datasets imposes significant storage and computational burdens on training deep learning models. Dataset distillation methods, particularly those based on sample generation, aim to condense large original datasets into compact synthetic sets while preserving essential information....
Xiao Cui, Yu-Lei Qin, Mo Zhu et al.· IEEE Transactions on Pattern...· 1 citation
Reinforcement learning (RL) is promising for spatial text-to-image generation, but existing work largely focuses on policy optimization, assuming accurate rewards and effective online rollouts. We observe in-domain gains with out-of-domain degradation, exposing two limitations: unreliable spatial rewards in open-world...
Xiaofeng Tan, Jia-Xin Lin, Haobo Yuan et al.· 0 citations
Distribution matching distillation (DMD) provides a general framework for few-step diffusion generation, but its modern text-to-image instantiations have been developed primarily around latent diffusion. It therefore overlooks key properties and design opportunities of native RGB. We revisit two DMD interfaces for pixe...
Xin Lin, Zhi-Fei Zhang, Yu-Qian Zhou et al.· 0 citations
This work identifies direct output access to the image statistics being corrected as a key factor governing when adversarial post-training succeeds, the first systematic study of adversarial post-training for pixel diffusion.
Xin Lin, Zhi-Fei Zhang, Yu-Qian Zhou et al.· 0 citations
DG-Mem is proposed, a dual-grained agentic memory framework that augments a frozen MLLM with a non-parametric, externally stored memory built once from training-time rollouts and consulted read-only at test time, and improves consistently over no-memory and competitive memory baselines.
Jie-Ke Wang, Tian-Cheng Shen, Yibo Yang et al.· 0 citations
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