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Title: Evaluation of generative networks through their data augmentation capacity

Paper content:
Generative networks are known to be difficult to assess.
Recent works on generative models, especially on generative adversarial networks, produce nice samples of varied categories of images.
But the validation of their quality is highly dependent on the method used.
A good generator should generate data which contain meaningful and varied information and that fit the distribution of a dataset.
This paper presents a new method to assess a generator.
Our approach is based on training a classifier with a mixture of real and generated samples.
We train a generative model over a labeled training set, then we use this generative model to sample new data points that we mix with the original training data.
This mixture of real and generated data is thus used to train a classifier which is afterwards tested on a given labeled test dataset.
We compare this result with the score of the same classifier trained on the real training data mixed with noise.
By computing the classifier's accuracy with different ratios of samples from both distributions (real and generated) we are able to estimate if the generator successfully fits and is able to generalize the distribution of the dataset.
Our experiments compare the result of different generators from the VAE and GAN framework on MNIST and fashion MNIST dataset.
Generative network approaches have been widely used to generate samples in recent years.
Methods such as GAN BID2 , WGAN BID0 , CGAN BID6 , CVAE BID15 and VAE BID3 have produced nice samples on various image datasets such as MNIST, bedrooms BID10 or imageNet BID8 .One
commonly accepted tool to evaluate a generative model trained on images is visual assessment to validate the realistic character of samples. One
case of this method is called 'visual Turing tests', in which samples are visualized by humans who try to guess if the images are generated or not. It
has been used to assess generative models of images from ImageNet BID1 and also on digit images BID4 . BID13
proposes to automate this method with the inception score, which replaces the human judgment by the use of a pretrained classifier to assess the variability of the samples with an entropy measure of the predicted classes and the confidence in the prediction. Unfortunately
, those two methods do not indicate if the generator collapses to a particular mode of the data distribution. Log-likelihood
based evaluation metrics were widely used to evaluate generative models but as shown in Lucas BID5 , those evaluations can be misleading in high dimensional cases.The solution we propose to estimate both sample quality and global fit of the data distribution is to incorporate generated data into the training phase of a classifier before evaluating it. Using generated
samples for training has several advantages over using only the original dataset. First, it can make
training more efficient when the amount of data is low. As shown in BID7 ,
where the conditional distribution P (Y |X)(X represents the samples and Y the classes) learned by a generative model is compared to the same conditional distribution learned by a discriminative model, the generative model performs better in learning this conditional distribution by regularizing the model when the amount of data is low. Secondly, once the
generative model is trained, it can sample as much images as needed and can produce interpolations between two images which will induce less risk of overfitting on the data. Other works use generative
models for data augmentation BID11 or to produce labeled data BID14 in order to improve the training of discriminative models, but their intention is not to use it to evaluate or compare generative neural networks.Our method evaluates the quality of a generative model by assessing its capacity to fit the real distribution of the data. For this purpose, we use the
samples generated by a given trained generative model. Our work aims to show how this
data augmentation can benefit the training of a classifier and how we can use this benefit as an evaluation tool in order to assess a generative model. This method evaluates whether
the information of the original distribution is still present in the generated data and whether the generator is able to produce new samples that are eventually close to unseen data. We compare classifiers trained
over mixtures of generated and real data with varying ratios and with varying total amounts of data. This allows us to compare generative
models in various data settings (i.e., when there is few or many data points).The next section will present the related
work on generative models, the exploitation of the generated samples and their evaluation. We then present our generative model evaluation
framework before presenting experimental results on several generative models with different datasets.
This paper introduces a new method to assess and compare the performances of generative models on various labeled datasets.
By training a classifier on several mixture of generated and real data we can estimate the ability of a generative model to generalize.
When addition of generated data into the training set achieved better data augmentation than traditional data augmentation as Gaussian noise or random dropout, it demonstrates the ability of generative models to create meaningful samples.
By varying the number of training data, we compute a data augmentation capacity Ψ G for each model on MNIST and fashion-MNIST datasets.
Ψ G is a global estimation of the generalization capacity of a generative model on a given dataset.
The results presented here are produced on image datasets but this method can be used on all kinds of datasets or generative models as long as labeled data is available.A ADDITIONAL RESULTS It represents the overfitting capacity of a VAE.
All four samples set look good, but for example, the top left trained with only 50 different data often produce similar images (as the samples on top right trained with 100 images).
When the number of training images increases the variability seems good afterwards but as we can see in FIG2 when τ = 1 the generator generalizes better the distribution when n is < 1000 than when > 1000 is high.
Relative accuracy improvement between the baseline trained on original data and the accuracy with generated or noise data augmentation in training.
τ is the ratio between the number of generated data and the total number of data used for training.

assistant: text: Evaluating generative networks through their data augmentation capacity on discrimative models.