Interpreting Adversarially Trained Convolutional Neural Networks
个人总结： 其实说白了就是： AT-CNNs are more biased towards global structures, such as shapes and edges。 AT-CNN对图像的整体结构，比如形状，边缘更加敏感，可以更好的捕捉到这些信息。 AT-CNNs are less sensitive to the texture distortion and foc
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相关文章
- 1. 可解释的对抗训练网络模型：Interpreting Adversarially Trained Convolutional Neural Networks-ICML 2019
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- 3. 【论文阅读笔记】Analyzing and Interpreting Convolutional Neural Networks in NLP
- 4. Convolutional Neural Networks: Application
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- 6. Convolutional Neural Networks-Week1
- 7. Convolutional Neural Networks
- 8. Orthogonal Convolutional Neural Networks
- 9. Convolutional Neural Networks-Week2
- 10. Convolutional Neural Networks - Week4