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Found 6 papers, 0 papers with code
Test-time Adaptation Meets Image Enhancement: Improving Accuracy via Uncertainty-aware Logit Switching
We hypothesize that enhancing the input image reduces prediction's uncertainty and increase the accuracy of TTA methods.
Test-time Similarity Modification for Person Re-identification toward Temporal Distribution Shift
However, the uncertainty cannot be computed in the same way as classification in re-id since it is an open-set task, which does not share person labels between training and testing.
Adaptive Random Feature Regularization on Fine-tuning Deep Neural Networks
To this end, AdaRand minimizes the gap between feature vectors and random reference vectors that are sampled from class conditional Gaussian distributions.
Fast Regularized Discrete Optimal Transport with Group-Sparse Regularizers
Regularized discrete optimal transport (OT) is a powerful tool to measure the distance between two discrete distributions that have been constructed from data samples on two different domains.
Covariance-aware Feature Alignment with Pre-computed Source Statistics for Test-time Adaptation to Multiple Image Corruptions
Test-time adaptation (TTA), which aims to adapt models without accessing the training dataset, is one of the settings that can address this problem.
Learning Robust Convolutional Neural Networks with Relevant Feature Focusing via Explanations
Under this type of distribution shift, CNNs learn to focus on features that are not task-relevant, such as backgrounds from the training data, and degrade their accuracy on the test data.
