Data Augmentation Using Generative Adversarial Network

Effective training of neural networks requires much data. In the low-data regime, parameters are underdetermined, and learnt networks generalise poorly. Data Augmentation alleviates this by using existing data more effectively. However standard data augmentation produces only limited plausible alternative data. Given there is potential to generate a much broader set of augmentations, we design and train a generative model to do data augmentation. The model, based on image conditional Generative Adversarial Networks, takes data from a source domain and learns to take any data item and generalise it to generate other within-class data items. As this generative process does not depend on the classes themselves, it can be applied to novel unseen classes of data. We show that a Data Augmentation Generative Adversarial Network (DAGAN) augments standard vanilla classifiers well. We also show a DAGAN can enhance few-shot learning systems such as Matching Networks. We demonstrate these approaches on Omniglot, on EMNIST having learnt the DAGAN on Omniglot, and VGG-Face data. In our experiments we can see over 13% increase in accuracy in the low-data regime experiments in Omniglot (from 69% to 82%), EMNIST (73.9% to 76%) and VGG-Face (4.5% to 12%); in Matching Networks for Omniglot we observe an increase of 0.5% (from 96.9% to 97.4%) and an increase of 1.8% in EMNIST (from 59.5% to 61.3%).

https://arxiv.org/pdf/1711.04340.pdf

训练一个神经网络往往需要大量的数据,而传统的数据扩增方法无法提供足够高质量的数据。本文提出了一个基于cGAN的数据扩增方法,生成器模型通过输入一个作为参照物的真实数据,以及一个随机向量用于增加多样性,生成一个与输入真实数据同类别的合成数据。判别器通过输入真实数据和合成数据学习两种分布的差异,总体的目标是生成与真实数据分布相同的多样化合成数据。

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