Apr 30, 2020 · Abstract:Neural networks are known to suffer from catastrophic forgetting when trained on sequential datasets.
We introduce a recursive framework to train a model that is applicable to a broader scope of few-shot classification datasets by overcoming catastrophic ...
We introduce a recursive framework to train a model that is applicable to a broader scope of few-shot classification datasets by overcoming catastrophic ...
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This work demonstrates that the popular gradient-based model-agnostic meta-learning algorithm (MAML) indeed suffers from catastrophic forgetting and ...
Neural networks are known to suffer from catastrophic forgetting when trained on sequential datasets. While there have been numerous attempts to solve this ...
Oct 30, 2021 · This paper considers incremental few-shot learning, which requires a model to continually recognize new categories with only a few examples provided.
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[PDF] Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by ...
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This paper considers incremental few-shot learning, which requires a model to continually recognize new categories with only a few examples provided. Our.
We present an Augmentation-based Prediction Rectification (APR) approach to reduce the impact of catastrophic forgetting in the FSCIL setting.
The problem is that as we add more and more words, the model would start to fail on words that it learned earlier. This is called catastrophic forgetting. To ...