Generalized Reinforcement Meta Learning for Few-Shot Optimization

• URL: http://arxiv.org/abs/2005.01246v1
• Date: Mon, 4 May 2020 03:21:05 GMT
• Title: Generalized Reinforcement Meta Learning for Few-Shot Optimization
• Authors: Raviteja Anantha, Stephen Pulman, and Srinivas Chappidi
• Abstract summary: We present a generic and flexible Reinforcement Learning (RL) based meta-learning framework for the problem of few-shot learning. Our framework could be easily extended to do network architecture search.
• Score: 3.7675996866306845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a generic and flexible Reinforcement Learning (RL) based meta-learning framework for the problem of few-shot learning. During training, it learns the best optimization algorithm to produce a learner (ranker/classifier, etc) by exploiting stable patterns in loss surfaces. Our method implicitly estimates the gradients of a scaled loss function while retaining the general properties intact for parameter updates. Besides providing improved performance on few-shot tasks, our framework could be easily extended to do network architecture search. We further propose a novel dual encoder, affinity-score based decoder topology that achieves additional improvements to performance. Experiments on an internal dataset, MQ2007, and AwA2 show our approach outperforms existing alternative approaches by 21%, 8%, and 4% respectively on accuracy and NDCG metrics. On Mini-ImageNet dataset our approach achieves comparable results with Prototypical Networks. Empirical evaluations demonstrate that our approach provides a unified and effective framework.

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