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Rectification-based Knowledge Retention for Continual Learning
[article]
2021
arXiv
pre-print
Deep learning models suffer from catastrophic forgetting when trained in an incremental learning setting. In this work, we propose a novel approach to address the task incremental learning problem, which involves training a model on new tasks that arrive in an incremental manner. The task incremental learning problem becomes even more challenging when the test set contains classes that are not part of the train set, i.e., a task incremental generalized zero-shot learning problem. Our approach
arXiv:2103.16597v1
fatcat:fi56ub3kaffihpg2apixsvzmg4