Papers › Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
Yoonho Lee, Seungjin Choi
Gradient-based meta-learning methods leverage gradient descent to learn the commonalities among various tasks. While previous such methods have been successful in meta-learning tasks, they resort to simple gradient descent during meta-testing. Our primary contribution is the {\em MT-net}, which enables the meta-learner to learn on each layer's activation space a subspace that the task-specific learner performs gradient descent on. Additionally, a task-specific learner of an {\em MT-net} performs gradient descent with respect to a meta-learned distance metric, which warps the activation space to be more sensitive to task identity. We demonstrate that the dimension of this learned subspace reflects the complexity of the task-specific learner's adaptation task, and also that our model is less sensitive to the choice of initial learning rates than previous gradient-based meta-learning methods. Our method achieves state-of-the-art or comparable performance on few-shot classification and regression tasks.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Image Classification | Mini-Imagenet 5-way (1-shot) | MT-Net | Accuracy | 51.7 | #93 of 105 | Archive leaderboard | report |
| Few-Shot Image Classification | OMNIGLOT - 1-Shot, 20-way | MT-net | Accuracy | 96.2% | #9 of 20 | Archive leaderboard | report |
| Few-Shot Image Classification | OMNIGLOT - 1-Shot, 5-way | MT-net | Accuracy | 99.5 | #5 of 17 | 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.
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