84 citations · 375 across the 68 of their papers we have counts for
8 papers · 1 filter
DRDF: Determining the Importance of Different Multimodal Information with Dual-Router Dynamic Framework
Haiwen Hong, Xuan Jin, Yin Zhang +4
In multimodal tasks, we find that the importance of text and image modal information is different for different input cases, and for this motivation, we propose a high-performance…
RAMS-Trans: Recurrent Attention Multi-scale Transformer forFine-grained Image Recognition
Yunqing Hu, Xuan Jin, Yin Zhang +4
In fine-grained image recognition (FGIR), the localization and amplification of region attention is an important factor, which has been explored a lot by convolutional neural netwo…
Reliable Adversarial Distillation with Unreliable Teachers
Jianing Zhu, Jiangchao Yao, Bo Han +6
In ordinary distillation, student networks are trained with soft labels (SLs) given by pretrained teacher networks, and students are expected to improve upon teachers since SLs are…
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels
Jingfeng Zhang, Xilie Xu, Bo Han +4
Adversarial training (AT) formulated as the minimax optimization problem can effectively enhance the model's robustness against adversarial attacks. The existing AT methods mainly…
Guided Interpolation for Adversarial Training
Chen Chen, Jingfeng Zhang, Xilie Xu +4
To enhance adversarial robustness, adversarial training learns deep neural networks on the adversarial variants generated by their natural data. However, as the training progresses…
Understanding the Interaction of Adversarial Training with Noisy Labels
Jianing Zhu, Jingfeng Zhang, Bo Han +5
Noisy labels (NL) and adversarial examples both undermine trained models, but interestingly they have hitherto been studied independently. A recent adversarial training (AT) study…
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection
Hanshu Yan, Jingfeng Zhang, Gang Niu +3
We investigate the adversarial robustness of CNNs from the perspective of channel-wise activations. By comparing \textit{non-robust} (normally trained) and \textit{robustified} (ad…
Learning Diverse-Structured Networks for Adversarial Robustness
Xuefeng Du, Jingfeng Zhang, Bo Han +5
In adversarial training (AT), the main focus has been the objective and optimizer while the model has been less studied, so that the models being used are still those classic ones…