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. 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%).
Code (7)
Tasks
Data AugmentationFew-Shot LearningGenerative Adversarial NetworkSimilar Papers 제목 키워드 기반
Toward Understanding Generative Data Augmentation
Generative data augmentation, which scales datasets by obtaining fake labeled examples from a trained conditional generative model, boosts classification performance in various learning tasks including (semi-)supervised …
Improving Android Malware Detection Through Data Augmentation Using Wasserstein Generative Adversarial Networks
Generative Adversarial Networks (GANs) have demonstrated their versatility across various applications, including data augmentation and malware detection. This research explores the effectiveness of utilizing GAN-generat…
Android Malware DetectionData AugmentationGenerative Adversarial NetworkMalware DetectionA Review on Generative Adversarial Networks for Data Augmentation in Person Re-Identification Systems
Interest in automatic people re-identification systems has significantly grown in recent years, mainly for developing surveillance and smart shops software. Due to the variability in person posture, different lighting co…
Data AugmentationPerson Re-IdentificationPose TransferStyle TransferTTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models
Data augmentation has been established as an efficacious approach to supplement useful information for low-resource datasets. Traditional augmentation techniques such as noise injection and image transformations have bee…
Data AugmentationDiversitydomain classificationImage CaptioningData Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Network
Deep Neural Networks (DNNs) show a significant impact on medical imaging. One significant problem with adopting DNNs for skin cancer classification is that the class frequencies in the existing datasets are imbalanced. T…
Cancer ClassificationData AugmentationGeneral ClassificationGenerative Adversarial Network+1