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Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot Learning
[article]
2021
arXiv
pre-print
Zero-shot learning (ZSL) has been shown to be a promising approach to generalizing a model to categories unseen during training by leveraging class attributes, but challenges still remain. Recently, methods using generative models to combat bias towards classes seen during training have pushed the state of the art of ZSL, but these generative models can be slow or computationally expensive to train. Additionally, while many previous ZSL methods assume a one-time adaptation to unseen classes, in
arXiv:2102.11856v1
fatcat:cnmrgtvolvb3dedwsa5nvqwu3e