Domain Adaptation

Domain Adaptative Neighborhood Clustering via Entropy Optimization

Introduced by Saito et al. in Universal Domain Adaptation through Self Supervision

Domain Adaptive Neighborhood Clustering via Entropy Optimization (DANCE) is a self-supervised clustering method that harnesses the cluster structure of the target domain using self-supervision. This is done with a neighborhood clustering technique that self-supervises feature learning in the target. At the same time, useful source features and class boundaries are preserved and adapted with a partial domain alignment loss that the authors refer to as entropy separation loss. This loss allows the model to either match each target example with the source, or reject it as unknown.

Source: Universal Domain Adaptation through Self Supervision

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