4 papers
Images Speak Louder Than Scores: Failure Mode Escape for Enhancing Generative Quality
Jie Shao, Ke Zhu, Minghao Fu +2
Diffusion models have achieved remarkable progress in class-to-image generation. However, we observe that despite impressive FID scores, state-of-the-art models often generate dist…
Quantization without Tears
Minghao Fu, Hao Yu, Jie Shao +3
Deep neural networks, while achieving remarkable success across diverse tasks, demand significant resources, including computation, GPU memory, bandwidth, storage, and energy. Netw…
QwT-v2: Practical, Effective and Efficient Post-Training Quantization
Ningyuan Tang, Minghao Fu, Hao Yu +1
Network quantization is arguably one of the most practical network compression approaches for reducing the enormous resource consumption of modern deep neural networks. They usuall…
Minimal Interaction Separated Tuning: A New Paradigm for Visual Adaptation
Ningyuan Tang, Minghao Fu, Jianxin Wu
The rapid scaling of large vision pretrained models makes fine-tuning tasks more and more difficult on devices with low computational resources. We explore a new visual adaptation…