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Paper

Meta-Transfer Learning through Hard Tasks

MPS-Authors
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Schiele,  Bernt
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:1910.03648.pdf
(Preprint), 998KB

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Citation

Sun, Q., Liu, Y., Chen, Z., Chua, T.-S., & Schiele, B. (2019). Meta-Transfer Learning through Hard Tasks. Retrieved from http://arxiv.org/abs/1910.03648.


Cite as: http://hdl.handle.net/21.11116/0000-0005-5552-F
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 for each task. In addition, we introduce the hard task (HT) meta-batch scheme as an effective learning curriculum that further boosts the learning efficiency of MTL. We conduct few-shot learning experiments and report top performance for five-class few-shot recognition tasks on three challenging benchmarks: miniImageNet, tieredImageNet and Fewshot-CIFAR100 (FC100). 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.