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Found 4 papers, 1 papers with code
Neuron Activation Coverage: Rethinking Out-of-distribution Detection and Generalization
The out-of-distribution (OOD) problem generally arises when neural networks encounter data that significantly deviates from the training data distribution, i. e., in-distribution (InD).
Ranked #2 on
Out-of-Distribution Detection
on ImageNet-1k vs Textures
(AUROC metric)
Generalization Beyond Feature Alignment: Concept Activation-Guided Contrastive Learning
Specifically, by treating feature elements as neuron activation states, we show that conventional alignment methods tend to deteriorate the diversity of learned invariant features, as they indiscriminately minimize all neuron activation differences.
Privacy-Preserving Constrained Domain Generalization via Gradient Alignment
However, due to the issue of limited dataset availability and the strict legal and ethical requirements for patient privacy protection, the broad applications of medical imaging classification driven by DNN with large-scale training data have been largely hindered.
Neuron Coverage-Guided Domain Generalization
More specifically, by treating the DNN as a program and each neuron as a functional point of the code, during the network training we aim to improve the generalization capability by maximizing the neuron coverage of DNN with the gradient similarity regularization between the original and augmented samples.
