14 citations · 20 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
Sampling to Distill: Knowledge Transfer from Open-World Data
Yuzheng Wang, Zhaoyu Chen, Jie Zhang +7
Data-Free Knowledge Distillation (DFKD) is a novel task that aims to train high-performance student models using only the pre-trained teacher network without original training data…
Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation
Yuzheng Wang, Zhaoyu Chen, Dingkang Yang +4
Adversarial Robustness Distillation (ARD) is a promising task to solve the issue of limited adversarial robustness of small capacity models while optimizing the expensive computati…
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…
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…