Papers › EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization

EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization

19 Jun 2021NeurIPS 2021 12arXiv:2106.10575archive 2025-07-28

Ondrej Bohdal, Yongxin Yang, Timothy Hospedales

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.

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convert_examples_to_features ondrejbohdal/evograd/CrossLingualLearningMetaXL/mtrain.py official repository ran · our draft was wrong MIT (permissive) · c4806e075ebe8fb0 · report
extract_args_from_json ondrejbohdal/evograd/LabelNoiseMetaWeightNet/meta-weight-net-label-noise.py official repository ran · our draft was wrong MIT (permissive) · 25b7c604b7346978 · report
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Cross-Domain Few-ShotFew-Shot LearningHyperparameter OptimizationMeta-Learningcross-domain few-shot learning

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