6 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…
Who Reasons in the Large Language Models?
Jie Shao, Jianxin Wu
Despite the impressive performance of large language models (LLMs), the process of endowing them with new capabilities--such as mathematical reasoning--remains largely empirical an…
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…
All You Need in Knowledge Distillation Is a Tailored Coordinate System
Junjie Zhou, Ke Zhu, Jianxin Wu
Knowledge Distillation (KD) is essential in transferring dark knowledge from a large teacher to a small student network, such that the student can be much more efficient than the t…
Diffusion Product Quantization
Jie Shao, Hanxiao Zhang, Jianxin Wu
In this work, we explore the quantization of diffusion models in extreme compression regimes to reduce model size while maintaining performance. We begin by investigating classical…