Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation Learning. (arXiv:2206.10137v2 [cs.CV] UPDATED)

Contrastive self-supervised learning methods learn to map data points such as
images into non-parametric representation space without requiring labels. While
highly successful, current methods require a large amount of data in the
training phase. In situations where the target training set is limited in size,
generalization is known to be poor. Pretraining on a large source data set and
fine-tuning on the target samples is prone to overfitting in the few-shot
regime, where only a small number of target samples …

arxiv cv domain adaptation learning representation representation learning unsupervised