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Robustness, Privacy, and Generalization of Adversarial Training
Fengxiang He, Shaopeng Fu, Bohan Wang +1
Adversarial training can considerably robustify deep neural networks to resist adversarial attacks. However, some works suggested that adversarial training might comprise the priva…
Comprehensive Graph-conditional Similarity Preserving Network for Unsupervised Cross-modal Hashing
Jun Yu, Hao Zhou, Yibing Zhan +1
Unsupervised cross-modal hashing (UCMH) has become a hot topic recently. Current UCMH focuses on exploring data similarities. However, current UCMH methods calculate the similarity…
SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data
Shaoli Huang, Xinchao Wang, Dacheng Tao
Data mixing augmentation has proved effective in training deep models. Recent methods mix labels mainly based on the mixture proportion of image pixels. As the main discriminative…
Toward Trainability of Quantum Neural Networks
Kaining Zhang, Min-Hsiu Hsieh, Liu Liu +1
Quantum Neural Networks (QNNs) have been recently proposed as generalizations of classical neural networks to achieve the quantum speed-up. Despite the potential to outperform clas…
A Survey on Visual Transformer
Kai Han, Yunhe Wang, Hanting Chen +10
Transformer, first applied to the field of natural language processing, is a type of deep neural network mainly based on the self-attention mechanism. Thanks to its strong represen…
Progressive One-shot Human Parsing
Haoyu He, Jing Zhang, Bhavani Thuraisingham +1
Prior human parsing models are limited to parsing humans into classes pre-defined in the training data, which is not flexible to generalize to unseen classes, e.g., new clothing in…