Machine learning models require for their training a vast amount of data that we not always have. One possible solution would be to collect more data samples, but this would take a lot of time.

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To combat it, we propose Differentiable Augmentation (DiffAugment), a simple method that improves the data efficiency of GANs by imposing various types of differentiable augmentations on both real and … After the autoencoder’s training, the knowledge about the images features is transferred into GAN. This handover of information is ensured by GAN being initialised with the autoencoder’s weights. Previous attempts to directly augment the training data manipulate the distribution of real images, yielding little benefit; DiffAugment enables us to adopt the differentiable augmentation for the generated samples, effectively stabilizes training, and leads to better convergence. 2021-04-14 Differentiable Augmentation for Data-Efficient GAN Training Review 1 Summary and Contributions : The authors propose DiffAugment which promotes data efficiency of GANs so as to improve the effectiveness of GANs especially on limited data. 100% training data 20% training data 10% training data FID ↓ StyleGAN2 (baseline) + DiffAugment (ours) 36.0 14.5 15 20 30 35 StyleGAN2 (baseline) + DiffAugment (ours) Our Results CIFAR-10 It can be used to significantly improve the data efficiency for GAN training. We have provided DiffAugment-stylegan2 (TensorFlow) and DiffAugment-stylegan2-pytorch, DiffAugment-biggan-cifar (PyTorch) for GPU training, and DiffAugment-biggan-imagenet (TensorFlow) for TPU training.

Text-to-Speech synthesis (TTS) based data augmentation is a relatively new ( GAN) and multi-style training (MTR) to increase acoustic di- versity in the 

The performance merit of proposed MG-GAN was compared with KNN and Basic GAN. Before data augmentation, we split the data into the train and validation set so that no samples in the validation set have been used for data augmentation. train,valid=train_test_split(tweet,test_size= 0.15) Now, we can do data augmentation of the training dataset. I have chosen to generate 300 samples from the positive class. The performance of generative adversarial networks (GANs) heavily deteriorates given a limited amount of training data.

On data augmentation for gan training

o AR, Augmented Reality: Datorgenererad information presenteras överlagrat på reinforcement learning, ha kontinuerlig tillgång till mängder av data (big data) från nyligen uppmärksammat exempel på GAN tillämpning är GPT‐236 från 

On data augmentation for gan training

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On data augmentation for gan training

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It increases the amount of training data in a way that is natural/useful for the domain, and thus reduces over-fitting when training deep neural networks with millions of parameters. In the image domain, a variety of augmentation techniques have been proposed to Data augmentation is frequently used to increase the effective training set size when training deep neural networks for supervised learning tasks. This technique is particularly beneficial when the size of the training set is small.
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collapse during GAN training.

We then propose a principled framework, termed Data Augmentation Optimized for GAN (DAG), to enable the use of augmented data in GAN training to improve the learning of the original distribution. We provide theoretical analysis to show that using our proposed DAG aligns with the original GAN in minimizing the Jensen–Shannon (JS) divergence

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Adding GAN generated data can be more beneficial than adding more original data, and leads to more stability in training Recursive training of GANs failed to yield performance increase References: [1] Fabio Henrique Kiyoiti dos Santos Tanaka and Claus Aranha. Data Augmentation Using GANs. Paper: https://arxiv.org/pdf/2006.10738.pdf Code: https://github.com/mit-han-lab/data-efficient-gans The performance of generative adversarial networks (GANs) heavily deteriorates given a limited amount of training data. This is mainly because the discriminatorsis memorizing the exact training set. To combat it, we propose Differentiable Augmentation (DiffAugment), a simple method that improves the data efficiency of GANs by imposing various types of differentiable augmentations on both real SS-GAN [6] we achieve the best FID of 14:7 for the unsupervised setting on CIFAR10, which is on par with the results achieved by large scale BigGAN training [4] using label supervision. 2 Related Work Many recent works have focused on improving the stability of GAN training and the overall visual quality of generated samples [28, 24, 35, 4].