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A formal approach to good practices in Pseudo-Labeling for Unsupervised Domain Adaptive Re-Identification
(ii) General good practices for Pseudo-Labeling, directly deduced from the interpretation of the proposed theoretical framework, in order to improve the target re-ID performance.
Improving Unsupervised Domain Adaptive Re-Identification via Source-Guided Selection of Pseudo-Labeling Hyperparameters
However, the effectiveness of these approaches heavily depends on the choice of some hyperparameters (HP) that affect the generation of pseudo-labels by clustering.
Unsupervised Domain Adaptation for Person Re-Identification through Source-Guided Pseudo-Labeling
A challenge for re-ID is the performance preservation when a model is used on data of interest (target data) which belong to a different domain from the training data domain (source data).
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