p

ALT Overview of the proposed adaptation approach. (a) Target samples are clustered based on the features extracted from the pretrained source model, to provide an initial pseudo-labels assignment. Next, clustering allows the identification of class centroids (coloured shapes), which are matched against the most similar prototypes of shared classes. Since the target domain has more classes than the source domain, some clusters are left out from this matching, which will be treated as target-private classes ( YELLOW clusters). (b) After the refining of the pseudo-label, its uncertainty is estimated using two approaches. Neighbours consensus: the uncertainty is determined by analysing the consensus of neighbours on the refined pseudo-label. Class separation: the model uncertainty is estimated based on the distance w.r.t. the two closest class prototypes. A novel contrastive loss (NL-InfoNCELoss) gathers same-class samples in the features space, while being robust to noisy pseudo-labels.