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Found 2 papers, 1 papers with code
Test-Time Adaptation via Self-Training with Nearest Neighbor Information
To overcome this limitation, we propose a novel test-time adaptation method, called Test-time Adaptation via Self-Training with nearest neighbor information (TAST), which is composed of the following procedures: (1) adds trainable adaptation modules on top of the trained feature extractor; (2) newly defines a pseudo-label distribution for the test data by using the nearest neighbor information; (3) trains these modules only a few times during test time to match the nearest neighbor-based pseudo label distribution and a prototype-based class distribution for the test data; and (4) predicts the label of test data using the average predicted class distribution from these modules.
Few-Example Clustering via Contrastive Learning
We propose Few-Example Clustering (FEC), a novel algorithm that performs contrastive learning to cluster few examples.
