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Edge Detectors Can Make Deep Convolutional Neural Networks More Robust

Autor: Ding, Jin, Zhao, Jie-Chao, Sun, Yong-Zhi, Tan, Ping, Wang, Jia-Wei, Ma, Ji-En, Fang, You-Tong

Deep convolutional neural networks (DCNN for short) are vulnerable to examples with small perturbations. Improving DCNN's robustness is of great significance to the safety-critical applications, such as autonomous driving and industry automation. Ins

Externí odkaz: http://arxiv.org/abs/2402.16479

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Improving the Robustness of Deep Convolutional Neural Networks Through Feature Learning

Autor: Ding, Jin, Zhao, Jie-Chao, Sun, Yong-Zhi, Tan, Ping, Ma, Ji-En, Fang, You-Tong

Deep convolutional neural network (DCNN for short) models are vulnerable to examples with small perturbations. Adversarial training (AT for short) is a widely used approach to enhance the robustness of DCNN models by data augmentation. In AT, the DCN

Externí odkaz: http://arxiv.org/abs/2303.06425

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Learning Feature Fusion for Unsupervised Domain Adaptive Person Re-identification

Autor: Ding, Jin, Zhou, Xue

Unsupervised domain adaptive (UDA) person re-identification (ReID) has gained increasing attention for its effectiveness on the target domain without manual annotations. Most fine-tuning based UDA person ReID methods focus on encoding global features

Externí odkaz: http://arxiv.org/abs/2205.09495

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