most citedAdversarial Contrastive Distillation with Adaptive Denoising

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Self-Cooperation Knowledge Distillation for Novel Class Discovery

Yuzheng Wang, Zhaoyu Chen, Dingkang Yang +2

Novel Class Discovery (NCD) aims to discover unknown and novel classes in an unlabeled set by leveraging knowledge already learned about known classes. Existing works focus on inst…

cs.CV2024

De-confounded Data-free Knowledge Distillation for Handling Distribution Shifts

Yuzheng Wang, Dingkang Yang, Zhaoyu Chen +5

Data-Free Knowledge Distillation (DFKD) is a promising task to train high-performance small models to enhance actual deployment without relying on the original training data. Exist…

cs.CV2023

On the Importance of Spatial Relations for Few-shot Action Recognition

Yilun Zhang, Yuqian Fu, Xingjun Ma +4

Deep learning has achieved great success in video recognition, yet still struggles to recognize novel actions when faced with only a few examples. To tackle this challenge, few-sho…

cs.CV2023

Explicit and Implicit Knowledge Distillation via Unlabeled Data

Yuzheng Wang, Zuhao Ge, Zhaoyu Chen +4

Data-free knowledge distillation is a challenging model lightweight task for scenarios in which the original dataset is not available. Previous methods require a lot of extra compu…

cs.CV20231 cited

Adversarial Contrastive Distillation with Adaptive Denoising

Yuzheng Wang, Zhaoyu Chen, Dingkang Yang +4

Adversarial Robustness Distillation (ARD) is a novel method to boost the robustness of small models. Unlike general adversarial training, its robust knowledge transfer can be less…