Edge intelligence requires models to sense continuously in real time and to keep adapting on-device, all under tight compute, energy, and memory budgets. Although spiking neural networks (SNNs) enable efficient event-driven inference, standard surrogate-gradient backpropagation (BP) serializes updates and blocks ongoin...
Yan-Xun Zhang, Yi-Fei Wang, Chang-Ze Lv et al.· 0 citations
Diffusion distillation is widely adopted to accelerate sampling, and the resulting few-step models are broadly believed to match or even surpass their multi-step teachers in generation. However, standard evaluations such as GenEval2 typically draw only one sample per prompt, so improved scores may fail to reveal losses...
Yi-Fei Wang, Xiao-Yu Wu, Tsu-Jui Fu et al.· 0 citations
Standard video generators do not natively compact historical context into reusable memory tokens. As generation continues, the growing history makes it increasingly difficult to retain information from earlier frames due to long-context degradation. Key-frame-based approaches address this challenge by retaining selecte...
Xiao-Yu Wu, Wei-Hang Guo, Yi-Fei Wang et al.· 0 citations
Generative and representation learning remain asymmetrically connected: semantic representations are used to improve diffusion generation, whereas the models'own representations are often treated as a by-product of synthesis. We ask whether diffusion models can instead be trained to learn substantially stronger semanti...
Xiao-Yu Wu, Yi-Fei Wang, Chen Wei· 0 citations
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