Tensor Normalization and Full Distribution Training

Wolfgang Fuhl, Enkelejda Kasneci

Keywords: DNN, Normalization, Image overlay, Gradient from image, Classification, Robustness test, Multi Label

TL;DR: We reformulate the image classification to a multi label classification task and gain more robust nets.

Abstract: In this work, we introduce pixel wise tensor normalization, which is inserted after rectifier linear units and, together with batch normalization, provides a significant improvement in the accuracy of modern deep neural networks. In addition, this work deals with the robustness of networks.We show that the factorized superposition of images from the training set and the reformulation of the multi class problem into a multilabel problem yields significantly more robust networks. The reformulation and the adjustment of the multi class log loss also improves the results compared to the overlay with only one class as label. LinkToCodeBlind