4 citations · 8 across the 7 of their papers we have counts for
7 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…
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…
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…
DiffuLT: How to Make Diffusion Model Useful for Long-tail Recognition
Jie Shao, Ke Zhu, Hanxiao Zhang +1
This paper proposes a new pipeline for long-tail (LT) recognition. Instead of re-weighting or re-sampling, we utilize the long-tailed dataset itself to generate a balanced proxy th…
Low-rank Attention Side-Tuning for Parameter-Efficient Fine-Tuning
Ningyuan Tang, Minghao Fu, Ke Zhu +1
In finetuning a large pretrained model to downstream tasks, parameter-efficient fine-tuning (PEFT) methods can effectively finetune pretrained models with few trainable parameters,…
Rectify the Regression Bias in Long-Tailed Object Detection
Ke Zhu, Minghao Fu, Jie Shao +2
Long-tailed object detection faces great challenges because of its extremely imbalanced class distribution. Recent methods mainly focus on the classification bias and its loss func…
DTL: Disentangled Transfer Learning for Visual Recognition
Minghao Fu, Ke Zhu, Jianxin Wu
When pre-trained models become rapidly larger, the cost of fine-tuning on downstream tasks steadily increases, too. To economically fine-tune these models, parameter-efficient tran…