论文标题
通过频域正则化的强大学习
Robust Learning with Frequency Domain Regularization
论文作者
论文摘要
卷积神经网络在计算视觉的许多任务中都取得了出色的性能。但是，CNN倾向于偏向低频组件。他们优先考虑捕获低频模式，导致他们在遭受应用程序方案转换时失败。尽管对抗性示例意味着该模型对高频扰动非常敏感。在本文中，我们通过约束模型过滤器的频谱来引入一种新的正则化方法。与带限制训练不同，我们的方法认为有效的频率范围可能是在不同的层中纠缠而不是连续的，并通过反向传播端到端训练有效的频率范围。我们通过（1）防御对抗扰动来证明正则化的有效性； （2）减少不同体系结构中的概括差距； （3）在不进行微调的情况下提高转移学习方案的概括能力。
Convolution neural networks have achieved remarkable performance in many tasks of computing vision. However, CNN tends to bias to low frequency components. They prioritize capturing low frequency patterns which lead them fail when suffering from application scenario transformation. While adversarial example implies the model is very sensitive to high frequency perturbations. In this paper, we introduce a new regularization method by constraining the frequency spectra of the filter of the model. Different from band-limit training, our method considers the valid frequency range probably entangles in different layers rather than continuous and trains the valid frequency range end-to-end by backpropagation. We demonstrate the effectiveness of our regularization by (1) defensing to adversarial perturbations; (2) reducing the generalization gap in different architecture; (3) improving the generalization ability in transfer learning scenario without fine-tune.
