Publication Type
Journal Article
Version
acceptedVersion
Publication Date
3-2022
Abstract
Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples only, typical meta-learning models use shallow neural networks, thus limiting its effectiveness. In order to achieve top performance, some recent works tried to use the DNNs pre-trained on large-scale datasets but mostly in straight-forward manners, e.g., (1) taking their weights as a warm start of meta-training, and (2) freezing their convolutional layers as the feature extractor of base-learners. In this paper, we propose a novel approach called meta-transfer learning (MTL), which learns to transfer the weights of a deep NN for few-shot learning tasks. Specifically, meta refers to training multiple tasks, and transfer is achieved by learning scaling and shifting functions of DNN weights (and biases) for each task. To further boost the learning efficiency of MTL, we introduce the hard task (HT) meta-batch scheme as an effective learning curriculum of few-shot classification tasks. We conduct experiments for five-class few-shot classification tasks on three challenging benchmarks, miniImageNet, tieredImageNet, and Fewshot-CIFAR100 (FC100), in both supervised and semi-supervised settings. Extensive comparisons to related works validate that our MTL approach trained with the proposed HT meta-batch scheme achieves top performance. An ablation study also shows that both components contribute to fast convergence and high accuracy.
Keywords
few-shot learning, transfer learning, meta learning, image classification
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume
44
Issue
3
First Page
1443
Last Page
1456
ISSN
0162-8828
Identifier
10.1109/TPAMI.2020.3018506
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
SUN, Qianru; LIU, Yaoyao; CHEN, Zhaozheng; CHUA Tat-Seng; and SCHIELE Bernt.
Meta-transfer learning through hard tasks. (2022). IEEE Transactions on Pattern Analysis and Machine Intelligence. 44, (3), 1443-1456.
Available at: https://ink.library.smu.edu.sg/sis_research/5591
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TPAMI.2020.3018506