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Model-agnostic multi-stage loss optimization meta learning
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DOI：10.1007/s13042-021-01316-6.png)
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Abstract
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Model Agnostic Meta Learning (MAML) has become the most representative meta learning algorithm to solve few-shot learning problems. This paper mainly discusses MAML framework, focusing on the key problem of solving few-shot learning through meta learning. However, MAML is sensitive to the base model for the inner loop, and training instability occur during the training process, resulting in an increase of the training difficulty of the model in the process of training and verification process, causing degradation of model performance. In order to solve these problems, we propose a multi-stage loss optimization meta-learning algorithm. By discussing a learning mechanism for inner and outer loops, it improves the training stability and accelerates the convergence for the model. The generalization ability of MAML has been enhanced.
Keywords:
Meta learning
Few-shot learning
Training instability
Multi-stage loss optimization
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