title: Data Augmentation Generative Adversarial Networks
abstract: Effective training of neural networks requires much data. In the low-data regime,
  parameters are underdetermined, and learnt networks generalise poorly. Data
  Augmentation (Krizhevsky et al., 2012) 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%).
authors: Anthreas Antoniou Amos Storkey Harrison Edwards
hIndex: Amos Storkey 41