Papers › Meta-Learning with a Geometry-Adaptive Preconditioner

Meta-Learning with a Geometry-Adaptive Preconditioner

4 Apr 2023CVPR 2023 1arXiv:2304.01552archive 2025-07-28

Suhyun Kang, Duhun Hwang, Moonjung Eo, Taesup Kim, Wonjong Rhee

Model-agnostic meta-learning (MAML) is one of the most successful meta-learning algorithms. It has a bi-level optimization structure where the outer-loop process learns a shared initialization and the inner-loop process optimizes task-specific weights. Although MAML relies on the standard gradient descent in the inner-loop, recent studies have shown that controlling the inner-loop's gradient descent with a meta-learned preconditioner can be beneficial. Existing preconditioners, however, cannot simultaneously adapt in a task-specific and path-dependent way. Additionally, they do not satisfy the Riemannian metric condition, which can enable the steepest descent learning with preconditioned gradient. In this study, we propose Geometry-Adaptive Preconditioned gradient descent (GAP) that can overcome the limitations in MAML; GAP can efficiently meta-learn a preconditioner that is dependent on task-specific parameters, and its preconditioner can be shown to be a Riemannian metric. Thanks to the two properties, the geometry-adaptive preconditioner is effective for improving the inner-loop optimization. Experiment results show that GAP outperforms the state-of-the-art MAML family and preconditioned gradient descent-MAML (PGD-MAML) family in a variety of few-shot learning tasks. Code is available at: https://github.com/Suhyun777/CVPR23-GAP.

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Tasks

Few-Shot Image ClassificationFew-Shot LearningMeta-Learning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Few-Shot Image Classification Mini-Imagenet 5-way (1-shot) GAP Accuracy 54.86 #86 of 105 Archive leaderboard report
Few-Shot Image Classification Mini-Imagenet 5-way (1-shot) Approximate GAP Accuracy 53.52 #89 of 105 Archive leaderboard report
Few-Shot Image Classification Mini-Imagenet 5-way (5-shot) GAP Accuracy 71.55 #79 of 95 Archive leaderboard report
Few-Shot Image Classification Mini-Imagenet 5-way (5-shot) Approximate GAP Accuracy 70.75 #82 of 95 Archive leaderboard report
Few-Shot Image Classification Tiered ImageNet 5-way (1-shot) GAP Accuracy 57.6 #48 of 49 Archive leaderboard report
Few-Shot Image Classification Tiered ImageNet 5-way (1-shot) Approximate GAP Accuracy 56.86 #49 of 49 Archive leaderboard report
Few-Shot Image Classification Tiered ImageNet 5-way (5-shot) GAP Accuracy 74.9 #48 of 51 Archive leaderboard report
Few-Shot Image Classification Tiered ImageNet 5-way (5-shot) Approximate GAP Accuracy 74.41 #49 of 51 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

MAML

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