Papers › Decoder Choice Network for Meta-Learning
Decoder Choice Network for Meta-Learning
Jialin Liu, Fei Chao, Longzhi Yang, Chih-Min Lin, Qiang Shen
Meta-learning has been widely used for implementing few-shot learning and fast model adaptation. One kind of meta-learning methods attempt to learn how to control the gradient descent process in order to make the gradient-based learning have high speed and generalization. This work proposes a method that controls the gradient descent process of the model parameters of a neural network by limiting the model parameters in a low-dimensional latent space. The main challenge of this idea is that a decoder with too many parameters is required. This work designs a decoder with typical structure and shares a part of weights in the decoder to reduce the number of the required parameters. Besides, this work has introduced ensemble learning to work with the proposed approach for improving performance. The results show that the proposed approach is witnessed by the superior performance over the Omniglot classification and the miniImageNet classification tasks.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Few-Shot Image Classification | OMNIGLOT - 1-Shot, 20-way | DCN6-E | Accuracy | 99.11 | #2 of 20 | Archive leaderboard | report |
| Few-Shot Image Classification | OMNIGLOT - 1-Shot, 20-way | DCN4 | Accuracy | 98.8% | #3 of 20 | Archive leaderboard | report |
| Few-Shot Image Classification | OMNIGLOT - 1-Shot, 5-way | DCN6-E | Accuracy | 99.92% | #2 of 17 | Archive leaderboard | report |
| Few-Shot Image Classification | OMNIGLOT - 1-Shot, 5-way | DCN4 | Accuracy | 99.8% | #3 of 17 | Archive leaderboard | report |
| Few-Shot Image Classification | OMNIGLOT - 5-Shot, 20-way | DCN6-E | Accuracy | 99.63 | #2 of 19 | Archive leaderboard | report |
| Few-Shot Image Classification | OMNIGLOT - 5-Shot, 20-way | DCN4 | Accuracy | 99.5% | #3 of 19 | Archive leaderboard | report |
| Few-Shot Image Classification | OMNIGLOT - 5-Shot, 5-way | DCN6-E | Accuracy | 99.92% | #1 of 16 | Archive leaderboard | report |
| Few-Shot Image Classification | OMNIGLOT - 5-Shot, 5-way | DCN4 | Accuracy | 99.89% | #5 of 16 | 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
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections