1 citations · 1 across the 4 of their papers we have counts for
4 papers
Accelerating Augmentation Invariance Pretraining
Jinhong Lin, Cheng-En Wu, Yibing Wei +1
Our work tackles the computational challenges of contrastive learning methods, particularly for the pretraining of Vision Transformers (ViTs). Despite the effectiveness of contrast…
Block Pruning for Enhanced Efficiency in Convolutional Neural Networks
Cheng-En Wu, Azadeh Davoodi, Yu Hen Hu
This paper presents a novel approach to network pruning, targeting block pruning in deep neural networks for edge computing environments. Our method diverges from traditional techn…
Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?
Cheng-En Wu, Yu Tian, Haichao Yu +4
Vision-language models such as CLIP learn a generic text-image embedding from large-scale training data. A vision-language model can be adapted to a new classification task through…
Self-supervised Video Representation Learning with Cascade Positive Retrieval
Cheng-En Wu, Farley Lai, Yu Hen Hu +1
Self-supervised video representation learning has been shown to effectively improve downstream tasks such as video retrieval and action recognition. In this paper, we present the C…