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Domain segmentation and adjustment for generalized zero-shot learning
[article]
2020
arXiv
pre-print
In the generalized zero-shot learning, synthesizing unseen data with generative models has been the most popular method to address the imbalance of training data between seen and unseen classes. However, this method requires that the unseen semantic information is available during the training stage, and training generative models is not trivial. Given that the generator of these models can only be trained with seen classes, we argue that synthesizing unseen data may not be an ideal approach
arXiv:2002.00226v1
fatcat:aipudnnmfre7nlyss5m2tef7va