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Found 5 papers, 4 papers with code
Test-Time Adaptation in Point Clouds: Leveraging Sampling Variation with Weight Averaging
Test-Time Adaptation (TTA) addresses distribution shifts during testing by adapting a pretrained model without access to source data.
Harmonizing Flows: Leveraging normalizing flows for unsupervised and source-free MRI harmonization
Initially, a normalizing flow network is trained to capture the distribution characteristics of the source domain.
WATT: Weight Average Test-Time Adaptation of CLIP
In response, we present Weight Average Test-Time Adaptation (WATT) of CLIP, a pioneering approach facilitating full test-time adaptation (TTA) of this VLM.
GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised Point Cloud Learning in 3D
We introduce a pioneering approach to self-supervised learning for point clouds, employing a geometrically informed mask selection strategy called GeoMask3D (GM3D) to boost the efficiency of Masked Auto Encoders (MAE).
Harmonizing Flows: Unsupervised MR harmonization based on normalizing flows
In this paper, we propose an unsupervised framework based on normalizing flows that harmonizes MR images to mimic the distribution of the source domain.
