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Towards Lifelong Self-Supervision For Unpaired Image-to-Image Translation
[article]
2020
arXiv
pre-print
Unpaired Image-to-Image Translation (I2IT) tasks often suffer from lack of data, a problem which self-supervised learning (SSL) has recently been very popular and successful at tackling. Leveraging auxiliary tasks such as rotation prediction or generative colorization, SSL can produce better and more robust representations in a low data regime. Training such tasks along an I2IT task is however computationally intractable as model size and the number of task grow. On the other hand, learning
arXiv:2004.00161v1
fatcat:xwivijmhrrd3ng2cgxc2jhadza