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Cross-Entropy Loss Leads To Poor Margins
In this work, we study the binary classification of linearly separable datasets and show that linear classifiers could also have decision boundaries that lie close to their training dataset if cross-entropy loss is used for training.
Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples
We show that differential training can ensure a large margin between the decision boundary of the neural network and the points in the training dataset.
Adversarially Trained Autoencoders for Parallel-Data-Free Voice Conversion
Due to the use of a single encoder, our method can generalize to converting the voice of out-of-training speakers to speakers in the training dataset.
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