1 research outputs found
Generalized Reinforcement Meta Learning for Few-Shot Optimization
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.Comment: 10 pages, 4 figures, 4 tables, 2 algorithms, ICML conferenc