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Unsupervised Transfer Learning with Self-Supervised Remedy
[article]
2020
arXiv
pre-print
Generalising deep networks to novel domains without manual labels is challenging to deep learning. This problem is intrinsically difficult due to unpredictable changing nature of imagery data distributions in novel domains. Pre-learned knowledge does not transfer well without making strong assumptions about the learned and the novel domains. Different methods have been studied to address the underlying problem based on different assumptions, e.g. from domain adaptation to zero-shot and few-shot
arXiv:2006.04737v1
fatcat:jivttxerg5chhaxo4qdwvtokiq