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Found 9 papers, 3 papers with code
Bounding generalization error with input compression: An empirical study with infinite-width networks
Estimating the Generalization Error (GE) of Deep Neural Networks (DNNs) is an important task that often relies on availability of held-out data.
Monitoring Shortcut Learning using Mutual Information
The failure of deep neural networks to generalize to out-of-distribution data is a well-known problem and raises concerns about the deployment of trained networks in safety-critical domains such as healthcare, finance and autonomous vehicles.
Batch Normalization is a Cause of Adversarial Vulnerability
Batch normalization (batch norm) is often used in an attempt to stabilize and accelerate training in deep neural networks.
Adversarial Examples as an Input-Fault Tolerance Problem
We analyze the adversarial examples problem in terms of a model's fault tolerance with respect to its input.
A Rate-Distortion Theory of Adversarial Examples
The generalization ability of deep neural networks (DNNs) is intertwined with model complexity, robustness, and capacity.
Adversarial Training Versus Weight Decay
Performance-critical machine learning models should be robust to input perturbations not seen during training.
Predicting Adversarial Examples with High Confidence
It has been suggested that adversarial examples cause deep learning models to make incorrect predictions with high confidence.
Attacking Binarized Neural Networks
Neural networks with low-precision weights and activations offer compelling efficiency advantages over their full-precision equivalents.
The Ciona17 Dataset for Semantic Segmentation of Invasive Species in a Marine Aquaculture Environment
An original dataset for semantic segmentation, Ciona17, is introduced, which to the best of the authors' knowledge, is the first dataset of its kind with pixel-level annotations pertaining to invasive species in a marine environment.
