
Smoothness Analysis of Loss Functions of Adversarial Training
Deep neural networks are vulnerable to adversarial attacks. Recent studi...
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Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression
Adversarial training is actively studied for learning robust models agai...
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Constraining Logits by Bounded Function for Adversarial Robustness
We propose a method for improving adversarial robustness by addition of ...
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Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks
We propose Absum, which is a regularization method for improving adversa...
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Autoencoding Binary Classifiers for Supervised Anomaly Detection
We propose the Autoencoding Binary Classifiers (ABC), a novel supervised...
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Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder
One problem in the application of reinforcement learning to realworld p...
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FAVAE: Sequence Disentanglement using Information Bottleneck Principle
We propose the factorized action variational autoencoder (FAVAE), a stat...
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Variational Autoencoder with Implicit Optimal Priors
The variational autoencoder (VAE) is a powerful generative model that ca...
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Masanori Yamada
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