Name
Abstract | ||
|---|---|---|
Neural networks have been successfully applied in applications with a large amount of labeled data. However, the task of rapid generalization on new concepts with small training data while preserving performances on previously learned ones still presents a significant challenge to neural network models. In this work, we introduce a novel meta learning method, Meta Networks (MetaNet), that learns a meta-level knowledge across tasks and shifts its inductive biases via fast parameterization for rapid generalization. When evaluated on Omniglot and Mini-ImageNet benchmarks, our MetaNet models achieve a near human-level performance and outperform the baseline approaches by up to 6% accuracy. We demonstrate several appealing properties of MetaNet relating to generalization and continual learning. |
Year | Venue | DocType |
|---|---|---|
2017 | ICML | Conference |
Volume | Citations | PageRank |
abs/1703.00837 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tsendsuren Munkhdalai | 1 | 169 | 13.49 |
| Hong Yu | 2 | 1982 | 179.13 |