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Found 6 papers, 1 papers with code
Smoothness Analysis of Adversarial Training
We reveal that the constraint of adversarial attacks is one cause of the non-smoothness and that the smoothness depends on the types of the constraints.
Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression
We theoretically and experimentally confirm that the weight loss landscape becomes sharper as the magnitude of the noise of adversarial training increases in the linear logistic regression model.
Supervised Anomaly Detection based on Deep Autoregressive Density Estimators
We propose a supervised anomaly detection method based on neural density estimators, where the negative log likelihood is used for the anomaly score.
Autoencoding Binary Classifiers for Supervised Anomaly Detection
Since our approach becomes able to reconstruct the normal data points accurately and fails to reconstruct the known and unknown anomalies, it can accurately discriminate both known and unknown anomalies from normal data points.
Variational Autoencoder with Implicit Optimal Priors
However, KL divergence with the aggregated posterior cannot be calculated in a closed form, which prevents us from using this optimal prior.
Sigsoftmax: Reanalysis of the Softmax Bottleneck
On the basis of this analysis, we propose sigsoftmax, which is composed of a multiplication of an exponential function and sigmoid function.
