Gradient-based meta-learning and hyperparameter optimization have seen significant progress recently, enabling practical end-to-end training of neural networks together with many hyperparameters. Nevertheless, existing approaches are relatively expensive as they need to compute second-order derivatives and store a longer computational graph. This cost prevents scaling them to larger network architectures. We present EvoGrad, a new approach to meta-learning that draws upon evolutionary techniques to more efficiently compute hypergradients. EvoGrad estimates hypergradient with respect to hyperparameters without calculating second-order gradients, or storing a longer computational graph, leading to significant improvements in efficiency. We evaluate EvoGrad on three substantial recent meta-learning applications, namely cross-domain few-shot learning with feature-wise transformations, noisy label learning with Meta-Weight-Net and low-resource cross-lingual learning with meta representation transformation. The results show that EvoGrad significantly improves efficiency and enables scaling meta-learning to bigger architectures such as from ResNet10 to ResNet34.
This repository provides implementation for the experiments mentioned in the paper:
- Illustration using a 1-dimensional problem
- Rotation transformation
- Cross-domain few-shot classification via learned feature-wise transformation
- Label noise with MetaWeightNet
- Low-resource cross-lingual learning with MetaXL
The following libraries are generally needed for the experiments:
- PyTorch
- Higher
- Matplotlib
- Numpy
- Sklearn
- Tqdm
The versions and any additional libraries depend on the sub-problem (further details mentioned there).
If you find this useful for your research, please consider citing:
@inproceedings{bohdal2021evograd,
title={EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization},
author={Bohdal, Ondrej and Yang, Yongxin and Hospedales, Timothy},
booktitle={Advances in Neural Information Processing Systems},
year={2021}
}
This work was supported in part by the EPSRC Centre for Doctoral Training in Data Science, funded by the UK Engineering and Physical Sciences Research Council (grant EP/L016427/1) and the University of Edinburgh.