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Data augmentation is a useful technique to enlarge the size of the training set and prevent overfitting for different machine learning tasks when training data is scarce.
However, current data augmentation techniques rely heavily on human design and domain knowledge, and existing automated approaches are yet to fully exploit the latent features in the training dataset.
In this paper we propose  \textit{Parallel Adaptive GAN Data Augmentation}(PAGANDA), where the training set adaptively enriches itself with sample images automatically constructed from Generative Adversarial Networks (GANs) trained in parallel.
We demonstrate by experiments that our data augmentation strategy, with little model-specific considerations, can be easily adapted to cross-domain deep learning/machine learning tasks such as image classification and image inpainting, while significantly improving model performance in both tasks.
Our source code and experimental details are available at \url{https://github.com/miaojiang1987/k-folder-data-augmentation-gan/}.
Deep learning and machine learning models produce highly successful results when given sufficient training data.
However, when training data is scarce, overfitting will occur and the resulting model will generalize poorly.
Data augmentation(DA) ameliorates such issues by enlarging the original data set and making more effective use of the information in existing data.
Much prior work has centered on data augmentation strategies based on human design, including heuristic data augmentation strategies such as crop, mirror, rotation and distortion BID15 BID21 Proceedings of the 1 st Adaptive & Multitask Learning Workshop, Long Beach, California, 2019.
Copyright 2019 by the author(s).
et al., 2003) , interpolating through labeled data points in feature spaces BID5 , and adversarial data augmentation strategies based on BID22 BID8 .
These methods have greatly aided many deep learning tasks across several domains such as classification BID15 , image segmentation BID24 and image reconstruction/inpainting BID0 .Despite their success, these DA methods generally require domain-specific expert knowledge, manual operations and extensive amount of tuning depending on actual contexts BID3 BID6 .
In particular, the need to directly operate on existing data with domain knowledge prevents many previous data augmentation strategies from being applicable to more general settings.
To circumvent the need for specific domain knowledge in data augmentation, more recent work BID1 utilizes generative adversarial networks(GANs) BID10 to produce images that better encode features in the latent space of training data.
By alternatively optimizing the generator G and the discriminator D in the GAN, the GAN is able to produce images similar to the original data and effectively complement the training set.
It has been shown in experiments BID1 that GAN-based methods have indeed significantly boosted the performance of classifiers under limited data through automatic augmentation, but applications into other tasks are yet to be explored.
Furthermore, given the computational complexity of GANs, a natural way to reduce runtime is to consider parallelism BID13 BID7 .In view of these considerations, we propose in this paper Parallel Adaptive Generative Adversarial Network Data Augmentation(PAGANDA), where the training set adaptively enriches itself with sample images automatically constructed from Generative Adversarial Networks (GANs) trained in parallel.
Our contributions can be summarized as follows:• We propose a general adaptive black-box data augmentation strategy to diversify enhance training data, with no task-specific requirements.• We also include in our model a novel K-fold parallel framework, which helps make the most use of the existing data.• Experiments over various datasets and tasks demonstrate the effectiveness of our method in different context.
Data Augmentation(DA) Previous work on data augmentation can be classified into several groups.
Traditional Heuristic DA strategies such as crop, mirror, rotation and distortion BID15 BID21 have found their way in many deep classification tasks, but these method generally require domain-specific expert knowledge, manual operations and extensive amount of tuning depending on actual contexts BID3 BID6 .
Other DA methods used interpolation through labeled data points in feature spaces BID5 , but their dependence on class labels makes them inapplicable for tasks with weak or no supervision.
Adversarial Data Augmentation strategies BID22 BID8 choose from a select number of transformation operations to maximize the loss function of the end classification model involved in the task.
While good motivations for our methods, these methods make strong assumptions over the types of augmentation and are difficult to generalize.
BID18 BID4 transform the problem of choosing data augmentation strategies into a reinforcement learning policy search problems, but the choice of augmentation methods are still limited and the reinforcement learning algorithms have non-trivial computation overhead in addition to the main task.
ML problems with limited data For classfication with limited samples, BID19 proposed a convolutional neural network(CNN) to classify environmental sounds with limited samples.
Other algorithms have been proposed in BID9 BID25 ), yet many of them have assumptions/constraints that hurts their capacity for generalization.
For unsupervised learning models, recent research on sample complexity reduction in GAN training seeks to reparametrize the input noise using variational inference BID12 BID16 , but this method has severe mathematical limitation that prevents further generalization.
BID23 adopts transfer learning techniques to train a new GAN for limited data from a pre-trained GAN network.
While effective, this approach requires a pre-trained network in the first place and doesn't apply to the cases when data is scarce.
Parallel/Distributed GANs BID13 BID7 proposed the first distributed multidiscriminator generative adversarial models, yet these models require large datasets to train and have great computational complexity.
Moreover, these models are trained on
In this section we describe the details of Parallel Adaptive Generative Adversarial Network Data Augmentation (PAGANDA).
Our method consists of three interrelated components: generative data augmentation, parallel image generation with fold division, and adaptive weight adjustment.
To ensure that make full use of the information contained in the existing images, the first part of our method involves generative data augmentation, which constructs varied images given the training set by repeatedly generating samples from and adding samples to the training set using a generative adversarial net.
We start off with a limited training set, and consecutively run the generative adversarial net using the set.
After running a fixed number t of regular training epochs, we proceed to the augmentation epoch where the augmentation is conducted.
During the augmentation epoch, we extract a number of sample images from the generator G using standard procedures of sample image generation as described in BID17 BID11 .
For this batch of samples, we calculate the Inception Score(IS) as defined by BID20 to measure the authenticity of the images generated, which we denote as w.
Here the Inception Score provides a metric of the power of generator to produce realistic images: the higher the value of w, the more power the corresponding generator G. This batch of images are then added back into the original training set for subsequent augmentation epochs.
We alternate running t regular training epochs and the augmentation epoch for a fixed number of times or until convergence.
FIG0 is a flow-chart of our procedure.
Notice that our procedure is agnostic to the specific architecture of generative adversarial net used to augment the training data.
Since GANs capture the information in the latent feature space of the images and translate such information into generated images, our method has the capacity to reveal the potential features that are possibly not visually evident in the original training images.
Moreover, compared with many other data augmentation strategies which require one to pre-define the operations to be carried on the images, our method automatically enriches the training set and does not require human intervention.
The second part of our method consists of a parallel data generation strategy, inspired by K-fold cross validation in machine learning BID2 .
Dividing the training data into K folds at the beginning, we run in parallel K independent generators DISPLAYFORM0 .
Each generator G i is trained on one of data groups, and each data group i consists of K − 1 folds of the training set, except for the i-th fold.
After images are generated in each generator G i in the training epochs, the sample images produced by each generator during the augmentation epoch are fed back into the respective training data groups.
To allow for maximal usage of each generated image, we insert the images in a way such that the images generated by one generator G i are sent to the training data groups corresponding to all other K − 1 generators except for that corresponding to G i .
This is to insure that the different generators in parallel have access to as many varied data pieces as possible in subsequent steps of training, so as to prevent overfitting and bolster the robustness of our strategy.
FIG1 demonstrates our algorithm.
Furthermore, to determine which generators are the most effective in generating authentic images, we introduce adaptive generator weighting at each augmentation epoch.
At the initial stage, all the generators are treated equally.
Before the batch of sample images generated by one generator G i are sent to the data group corresponding to other K − 1 generators, we collect the inception scores {w i } K i=1 computed in section 3.1.
Since higher inception scores imply better performance of the generator, we define the generator weight p i of a generator G i as DISPLAYFORM0 and use this weight to determine how many images should be sampled from generator G i to be sent to other data groups for subsequent training in the very next augmentation epoch.
When the total number of samples to be collected from generators are fixed, this method enables generators with better realistic image generation power to contribute more to the future training data groups.
More realistic training sets thus augmented, in turn, exert more positive influence on the images to be generated.
Note that all three strategies introduced go hand in hand, with no need for model specific considerations.
As demonstrated by our experiments Section 4, training different GANs in parallel from different folds of data substantially boosts the quality of the training set and that of the generated images.
To illustrate the effectiveness of PAGANDA for multiple machine learning tasks, we have applied our data augmentation method to two tasks: image classification and image inpainting.
For image classification we constructed our dataset from Imagenet and Cifar-10 by randomly drawing 5000 images from each dataset respectively and applied PAGANDA on these reduced datasets.
The augmented datasets are then used to train an AlexNet CNN classifier, and the classification results are compared with the results obtained from an AlexNet trained on the corresponding original unaugmented datasets.
For image inpainting, we constructed our datasets from Places dataset.
We chose images from the Ocean subset from Places to obtain the reduced Places Dataset.
To ensure the parallelism of the experiments, we trained our model in a multi-threaded environment to make simultaneously training.
Under such a setting, all the data groups are trained at the same time, and each GAN model corresponding to each data group is trained in a separate thread.
All of our experiments are conducted on a server with Tesla-V GPU (32GB RAM, 7.8 TeraFLOPS) and Intel Xeon Processor E5 (2.00 GHz).
For our experiments on classification, we first augment the reduced Cifar-10 and reduced Imagenet datasets, and then train the CNN classifier with the augmented dataset.
The classifier accuracies with and without augmentation are listed in TAB0 below.
For the task of inpainting, we augment the reduced dataset constructed in the experiment.
Without loss of generality, we train a WGAN-GP model for inpainting from the augmented dataset.
We then select testing images that are not selected in the training set, and add to them gray masks covering the center part of these images.
We then applied our trained WGAN-GP to generate patches that cover the masked portion of the inpainting image.
Figure 4 lists a couple of generated images with and without augmentation.
Visual comparisons demonstrate the effectiveness of our method.
In sum, our paper shows that PAGANDA effectively improves the performances for different machine learning tasks with little task-specific considerations.
Our strategy is not only simple to implement, but also demonstrates capability to generate onto different settings since it does not require specific information about the task being analyzed.
As a further step, we are investigating the relationship between our proposed approach and other established methods.
We hope to apply our idea to other generative models such as VAE BID14 and further optimize our strategy using recent theoretical advances, and wish to investigate the scenarios where the tasks involved are interrelated.
Application wise, we are aiming to apply our parallel GAN model to multi-modal image synthesis/generation where training data is limited.

assistant: text: We present an automated adaptive data augmentation that works for multiple different tasks.