On Data Augmentation for GAN Training

نویسندگان

چکیده

Recent successes in Generative Adversarial Networks (GAN) have affirmed the importance of using more data GAN training. Yet it is expensive to collect many domains such as medical applications. Data Augmentation (DA) has been applied these In this work, we first argue that classical DA approach could mislead generator learn distribution augmented data, which be different from original data. We then propose a principled framework, termed Optimized for (DAG), enable use training improve learning distribution. provide theoretical analysis show our proposed DAG aligns with minimizing Jensen-Shannon (JS) divergence between and model Importantly, effectively leverages discriminator generator. conduct experiments apply models: unconditional GAN, conditional self-supervised CycleGAN datasets natural images images. The results achieves consistent considerable improvements across models. Furthermore, when used some models, system establishes state-of-the-art Frechet Inception Distance (FID) scores. Our code available.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

BAGAN: Data Augmentation with Balancing GAN

Image classification datasets are often imbalanced, characteristic that negatively affects the accuracy of deeplearning classifiers. In this work we propose balancing GANs (BAGANs) as an augmentation tool to restore balance in imbalanced datasets. This is challenging because the few minority-class images may not be enough to train a GAN. We overcome this issue by including during training all a...

متن کامل

Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification

In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs). We propose a training scheme that first uses classical data augmentation to enlarge the training set and then further enlarges the data size and its diversity by applying GAN techniques for synthetic data augmentation. Our method is demonstrated on a limited...

متن کامل

Training Data Augmentation for Low-Resource Morphological Inflection

This work describes the UoE-LMU submission for the CoNLL-SIGMORPHON 2017 Shared Task on Universal Morphological Reinflection, Subtask 1: given a lemma and target morphological tags, generate the target inflected form. We evaluate several ways to improve performance in the 1000-example setting: three methods to augment the training data with identical input-output pairs (i.e., autoencoding), a h...

متن کامل

Data Augmentation for Training of Noise Robust Acoustic Models

In this paper we analyse ways to improve the acoustic models based on deep neural networks with the help of data augmentation. These models are used for speech recognition in a priori unknown possibly noisy acoustic environment (with the presence of office or home noise, street noise, babble, etc.) and may deal with both the headset and distant microphone recordings. We compare acoustic models ...

متن کامل

Data augmentation for diffusions

The problem of formal likelihood-based (either classical or Bayesian) inference for discretely observed multi-dimensional diffusions is particularly challenging. In principle this involves data-augmentation of the observation data to give representations of the entire diffusion trajectory. Most currently proposed methodology splits broadly into two classes: either through the discretisation of ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: IEEE transactions on image processing

سال: 2021

ISSN: ['1057-7149', '1941-0042']

DOI: https://doi.org/10.1109/tip.2021.3049346