Feature Quantization for Defending Against Distortion of Images

CVPR 2018 Zhun SunMete OzayYan ZhangXing LiuTakayuki Okatani

In this work, we address the problem of improving robustness of convolutional neural networks (CNNs) to image distortion. We argue that higher moment statistics of feature distributions can be shifted due to image distortion, and the shift leads to performance decrease and cannot be reduced by ordinary normalization methods as observed in our experimental analyses... (read more)

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