Meta-learning Transferable Representations with a Single Target Domain
- URL: http://arxiv.org/abs/2011.01418v1
- Date: Tue, 3 Nov 2020 01:57:37 GMT
- Title: Meta-learning Transferable Representations with a Single Target Domain
- Authors: Hong Liu, Jeff Z. HaoChen, Colin Wei, Tengyu Ma
- Abstract summary: Fine-tuning and joint training do not always improve accuracy on downstream tasks.
We propose Meta Representation Learning (MeRLin) to learn transferable features.
MeRLin empirically outperforms previous state-of-the-art transfer learning algorithms on various real-world vision and NLP transfer learning benchmarks.
- Score: 46.83481356352768
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent works found that fine-tuning and joint training---two popular
approaches for transfer learning---do not always improve accuracy on downstream
tasks. First, we aim to understand more about when and why fine-tuning and
joint training can be suboptimal or even harmful for transfer learning. We
design semi-synthetic datasets where the source task can be solved by either
source-specific features or transferable features. We observe that (1)
pre-training may not have incentive to learn transferable features and (2)
joint training may simultaneously learn source-specific features and overfit to
the target. Second, to improve over fine-tuning and joint training, we propose
Meta Representation Learning (MeRLin) to learn transferable features. MeRLin
meta-learns representations by ensuring that a head fit on top of the
representations with target training data also performs well on target
validation data. We also prove that MeRLin recovers the target ground-truth
model with a quadratic neural net parameterization and a source distribution
that contains both transferable and source-specific features. On the same
distribution, pre-training and joint training provably fail to learn
transferable features. MeRLin empirically outperforms previous state-of-the-art
transfer learning algorithms on various real-world vision and NLP transfer
learning benchmarks.
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