Are LSTMs Good Few-Shot Learners?
Mike Huisman Thomas M. Moerland Aske Plaat Jan N. van Rijn
Deep learning requires large amounts of data to learn new tasks well,
limiting its applicability to domains where such data is available.
Meta-learning overcomes this limitation by learning how to learn. In 2001,
Hochreiter et al. showed that an LSTM trained with backpropagation across
different tasks is capable of meta-learning. Despite promising results of this
approach on small problems, and more recently, also on reinforcement learning
problems, the approach has received little attention in the supervised few-shot
learning setting. We revisit this approach and test it on modern few-shot
learning benchmarks. We find that LSTM, surprisingly, outperform the popular
meta-learning technique MAML on a simple few-shot sine wave regression
benchmark, but that LSTM, expectedly, fall short on more complex few-shot image
classification benchmarks. We identify two potential causes and propose a new
method called Outer Product LSTM (OP-LSTM) that resolves these issues and
displays substantial performance gains over the plain LSTM. Compared to popular
meta-learning baselines, OP-LSTM yields competitive performance on
within-domain few-shot image classification, and performs better in
cross-domain settings by 0.5% to 1.9% in accuracy score. While these results
alone do not set a new state-of-the-art, the advances of OP-LSTM are orthogonal
to other advances in the field of meta-learning, yield new insights in how LSTM
work in image classification, allowing for a whole range of new research
directions. For reproducibility purposes, we publish all our research code
publicly.