Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning

Namyeong Kwon, Hwidong Na, Gabriel Huang, Simon Lacoste-Julien

Keywords: Meta-learning, Few-shot learning, Out-of-domain, Uncertainty, Ensemble, Adversarial training, Stepsize optimization

Abstract: Model-agnostic meta-learning (MAML) is a popular method for few-shot learning but assumes that we have access to the meta-training set. In practice, training on the meta-training set may not always be an option due to data privacy concerns, intellectual property issues, or merely lack of computing resources. In this paper, we consider the novel problem of repurposing pretrained MAML checkpoints to solve new few-shot classification tasks. Because of the potential distribution mismatch, the original MAML steps may no longer be optimal. Therefore we propose an alternative meta-testing procedure and combine MAML gradient steps with adversarial training and uncertainty-based stepsize adaptation. Our method outperforms "vanilla" MAML on same-domain and cross-domains benchmarks using both SGD and Adam optimizers and shows improved robustness to the choice of base stepsize.

One-sentence Summary: We propose an alternative meta-testing procedure and combine MAML gradient steps with adversarial training and uncertainty-based stepsize adaptation.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Code: [ NamyeongK/USA_UFGSM](https://github.com/NamyeongK/USA_UFGSM)

Data: [Oxford 102 Flower](https://paperswithcode.com/dataset/oxford-102-flower), [mini-Imagenet](https://paperswithcode.com/dataset/mini-imagenet)

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2103.09027/code)