Data Augmentation Generative Adversarial Networks
Effective training of neural networks requires much data.In the low-data regime,parameters are underdetermined, and learnt networks generalise poorly.DataAugmentation alleviates this by using existing datamore effectively.However standard data augmentation produces only limitedplausible alternative data.Given there is potential to generate a much broader setof augmentations, we design and train a generative model to do data augmentation.The model, based on image conditional Generative Adversarial Networks, takesdata from a source domain and learns to take any data item and generalise itto generate other within-class data items.As this generative process does notdepend on the classes themselves, it can be applied to novel unseen classes of data.We show that a Data Augmentation Generative Adversarial Networkaugments standard vanilla classifiers well.We also show a DAGAN can enhancefew-shot learning systems such as Matching Networks.We demonstrate theseapproaches on Omniglot, on EMNIST having learnt the DAGAN on Omniglot, andVGG-Face data.In our experiments we can see over 13% increase in accuracy inthe low-data regime experiments in Omniglot, EMNIST and VGG-Face; in Matching Networks for Omniglot weobserve an increase of 0.5% and an increase of 1.8% inEMNIST.
Conditional GANs trained to generate data augmented samples of their conditional inputs used to enhance vanilla classification and one shot learning systems such as matching networks and pixel distanceThe authors propose a method to conduct data augmentation where the cross-class transformations are mapped to a low dimensional latent space using conditional GAN