MoSSDA is a two-stage framework that uses MMD, mixup-based supervised contrastive learning with a momentum encoder, and a frozen-features classifier to improve semi-supervised domain adaptation for time-series classification.
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MoSSDA: A Semi-Supervised Domain Adaptation Framework for Multivariate Time-Series Classification using Momentum Encoder
MoSSDA is a two-stage framework that uses MMD, mixup-based supervised contrastive learning with a momentum encoder, and a frozen-features classifier to improve semi-supervised domain adaptation for time-series classification.