We propose a meta-relation network to solve the few shot learning problem, where the classifier must learn to recognize new classes given only few examples from each. Meta-relation networks is based on relation networks and Model-Agnostic Meta-Learning (MAML) training methods, which can be trained end-to-end. After training with MAML algorithm, the meta-relation networks can adapt to learning quickly for a small number of samples from a new task with only a small amount of gradient step. It can also classify new classes of images by calculating the scores between query images and few examples of each new class. In experiments, we will demonstrate that the proposed approach leads to good performance on two few-shot image classification benchmarks.
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Meta-Relation Networks for Few Shot Learning
Semantic Scholar · Computer Science · 2019
Abstract
We propose a meta-relation network to solve the few shot learning problem, where the classifier must learn to recognize new classes given only few examples from each. Meta-relation networks is based on relation networks and Model-Agnostic Meta-Learning (MAML) training methods, which can be trained end-to-end. After training with MAML algorithm, the meta-relation networks can adapt to learning quickly for a small number of samples from a new task with only a small amount of gradient step. It can also classify new classes of images by calculating the scores between query images and few examples of each new class. In experiments, we will demonstrate that the proposed approach leads to good performance on two few-shot image classification benchmarks.