Improving the Realism of Synthetic Images

Vol. 1, Issue 1 July 2017


Most successful examples of neural nets today are trained with supervision. However, to achieve high accuracy, the training sets need to be large, diverse, and accurately annotated, which is costly. An alternative to labelling huge amounts of data is to use synthetic images from a simulator. This is cheap as there is no labeling cost, but the synthetic images may not be realistic enough, resulting in poor generalization on real test images. To help close this performance gap, we’ve developed a method for refining synthetic images to make them look more realistic. We show that training models on these refined images leads to significant improvements in accuracy on various machine learning tasks.

Overview

Training machine learning models on standard synthetic images is problematic as the images may not be realistic enough, leading the model to learn details present only in synthetic images and failing to generalize well on real images. One approach to bridge this gap between synthetic and real images would be to improve the simulator which is often expensive and difficult, and even the best rendering algorithm may still fail to model all the details present in the real images. This lack of realism may cause models to overfit to ‘unrealistic’ details in the synthetic images. Instead of modeling all the details in the simulator, could we learn them from data? To this end, we developed a method for refining synthetic images to make them look more realistic (Figure 1).

Figure 1. The task is to learn a model that improves the realism of synthetic images from a simulator using unlabeled real data, while preserving the annotation information.
Shows a refiner that uses several unlabeled real images and a synthetic image to create a more realistic looking synthetic image.

The goal of ‘improving realism’ is to make the images look as realistic as possible to improve the test accuracy. This means we want to preserve annotation information for training of machine learning models. For example, the gaze direction in Figure 1 should be preserved, as well as not generate any artifacts, as machine learning models could learn to overfit to them. We learn a deep neural network, which we call ‘refiner network’, that processes synthetic images to improve their realism.

To learn such a refiner network, we need some real images. One option would be to require pairs of real and synthetic images with pixel-wise correspondence, or real images with annotations—e.g. the gaze information in the case of eyes. This is arguably an easier problem, but such data is very difficult to collect. To create pixel-wise correspondence, we need to either render a synthetic image that corresponds to a given real image, or capture a real image that matches a rendered synthetic image. Could we instead learn this mapping without pixel-wise correspondence, or any labels for the real images? If so, we could just generate a bunch of synthetic images, capture real images of eyes, and without labeling any real images at all, learn this mapping—making the method cheap and easy to apply in practice.

To learn our refiner network in an unsupervised manner, we utilize an auxiliary discriminator network that classifies the real and the refined (or fake) images into two classes. The refiner network tries to fool this discriminator network into thinking the refined images are the real ones. Both the networks train alternately, and training stops when the discriminator cannot distinguish the real images from the fake ones. The idea of using an adversarial discriminator network is similar to the GANs (Generative Adversarial Networks [1]) approach that maps a random vector to an image such that the generated image is indistinguishable from the real ones. Our goal is to train a refiner network—a generator—that maps a synthetic image to a realistic image. Figure 2 shows an overview of the method.

Figure 2. Our refiner neural network, R, minimizes a combination of local adversarial loss and a ‘self-regularization’ term. The adversarial loss ‘fools’ the discriminator network, D, that classifies an image as real or refined. The self-regularization term minimizes the image difference between the synthetic and the refined images. The refiner network and the discriminator network are updated alternately.
Shows a refiner network that creates realistic looking synthetic images and then uses those as input to a discriminator network that then must distinguish between the synthetic image and a real one.

How do we preserve the annotations?

In addition to generating realistic images, the refiner network should preserve the annotation information of the simulator. For example, for gaze estimation the learned transformation should not change the gaze direction. This restriction is an essential ingredient to enable training a machine learning model that uses the refined images with the simulator’s annotations. To preserve the annotations of synthetic images, we complement the adversarial loss with a self-regularization L1 loss that penalizes large changes between the synthetic and refined images.

How do we prevent artifacts?

Making local changes

Another key requirement for the refiner network is that it should learn to model the real image characteristics without introducing any artifacts. When we train a single strong discriminator network, the refiner network tends to over-emphasize certain image features to fool the current discriminator network, leading to drifting and producing artifacts. A key observation is that any local patch sampled from the refined image should have similar statistics to a real image patch. Therefore, rather than defining a global discriminator network, we can define a discriminator network that classifies all local image patches separately (Figure 3). This division not only limits the receptive field, and hence the capacity of the discriminator network, but also provides many samples per image for learning the discriminator network. The refiner network is also improved by having multiple ‘realism loss’ values per image.

Shows one image that is input to a discriminator network and the resulting two dimensional probability map, width by height, that's used to compute the adversarial loss function.

Figure 3. Illustration of local adversarial loss. The discriminator network outputs a width by height probability map. The adversarial loss function is the sum of the cross-entropy losses over the local patches.

Using Generator History to Improve the Discriminator

The generator can fool the discriminator either with samples from a novel distribution or the target (real data) distribution. Generating from a novel distribution fools the discriminator only until the discriminator learns that novel distribution. The more useful way the generator can fool the discriminator is by generating from the target distribution.

Given these two ways of evolving, the easiest is usually to generate a novel output, which is what we observed when training the current generator and discriminator against each other. A simplified illustration of this unproductive sequence is shown in the left-hand side of Figure 4. The generator and discriminator distributions are shown in yellow and blue respectively.

By introducing a history which stores generator samples from previous generations (right-hand of Figure 4), the discriminator is less likely to forget the part of the space about which it has already learned. The more powerful discriminator helps the generator to move toward the target distribution faster. The illustration is a simplification and neglects to show that the distributions are complex and often disconnected regions. In practice however, a simple random replacement buffer captures enough diversity from previous generator distributions to prevent repetition by strengthening the discriminator. Our notion is that any refined image generated by the refiner network at any time during the entire training procedure is really a ‘fake’ image for the discriminator. We found that by constructing a mini-batch for D with half the samples from the history buffer and the other half from the current generator’s output (as shown in Figure 5) we can improve training.

Figure 4. An illustration of intuition behind using a history of images to improve the discriminator.
An animation showing that  the target distribution is reached by the discriminator only when the image history is taken into account.
Figure 5. Illustration of mini-batch with history for D. Each mini-batch consists of images from the current iteration of the generator, as well as from a buffer of previous fake images.
Shows the scheme for making a mini-batch for the discriminator network by taking some of the refined images from the generator and mixing them with some of the images from the buffer of refined images.

How does the training progress?

We first train the refiner network with only self-regularization loss, and introduce the adversarial loss after the refiner network starts producing blurry versions of the input synthetic images. Figure 6 shows the output of refiner network at various steps of training. In the beginning, it outputs a blurry image that becomes more and more realistic as training progresses. Figure 7 visualizes the discriminator and generator losses at different training iterations. Note that the discriminator loss is low in the beginning—meaning it can easily tell the difference between real and refined. Slowly, the discriminator loss increases and the generator loss decreases as training progress, generating more real images.

Figure 6. Output of the refiner network as training progresses. Starting with a blurry image, the network learns to model the details present in real images.
An animation that shows how an eye image changes as the refiner network learns to model the details found in real images of eyes.
Figure 7. Generator and discriminator losses as training progresses.
Two graphs. One shows discriminator loss as training steps increase. The other shows generator loss as training steps increase.

Is the self-regularization L1 loss limiting?

When the synthetic and real images have significant shift in the distribution, a pixel-wise L1 difference may be restrictive. In such cases, we can replace the identity map with an alternative feature transform, putting a self-regularizer on a feature space. These could be hand-tuned features, or learnt features such as a mid layer from VGGnet. For example, for color image refinement, the mean of RGB channels can generate realistic color images such as in Figure 8.

Figure 8. Example of self-regularization loss in feature space.
Shows an example of self regularizing color using the mean of RGB channels. Three images-real synthetic, and refined. After regularization, the color of the refined image looks much more natural than that of the synthetic image.

Are labels changed by the generator?

To verify that the labels do not change significantly, we manually drew ellipses on the synthetic and refined images, and computed the difference between their centers. In Figure 9, we show a scatter plot of fifty such center differences. The absolute difference between the estimated pupil center of synthetic and corresponding refined image is quite small: 1.1 plus or minus 0.8 px (eye width = fifty 5 px).

Figure 9. Scatter plot of distances between pupil centers of synthetic and real images.
Shows a plot of the distances between pupil centers of synthetic and real images, distances in pixels. Also shows three pairs of eye images that visually depict the difference between pupil centers.

How to set the hyper-parameters? Tips & tricks.

Initialization of G

First we initialized G only with the self-regularization loss so that it can start producing blurry version of the synthetic input. Typically it took five hundred to two thousand steps of G (without training D).

Different steps of G and D for each training iteration

We used different number of steps for the generator and discriminator in each training iteration. For hand-pose estimation using depth, we used 2 steps of G for each D step, and for eye gaze estimation experiment we ended up using fifty steps of G for each D step. We found the discriminator to converge more quickly compared to the generator, partly because of the batch-norm in the discriminator. So, we fixed the #D steps to 1, and started varying #G steps starting from a small number, slowly increasing it depending on the discriminator loss values.

Learning rate and stopping criteria

We found it helpful to keep learning rate very small (~0.0001) and train for a long time. This approach worked probably because it keeps both the generator or the discriminator from making sudden shifts which would leave the other behind. We found it difficult to stop the training by visualizing the training loss. Instead, we saved training images as training progresses, and stopped training when the refined images looked visually similar to real images.

Qualitative Results

To evaluate the visual quality of the refined images, we designed a simple user study where subjects were asked to classify images as real or refined synthetic. The subjects found it very difficult to tell the difference between the real and refined images. In our aggregate analysis, ten subjects chose the correct label five hundred seventeen times out of one thousand trials, meaning they were not able to reliably distinguish real images from refined synthetic ones. In contrast, when testing on original synthetic images vs real images, we showed ten real and ten synthetic images per subject, and the subjects chose correctly one hundred sixty two times out of two hundred trials. In Figure ten we show some example synthetic and corresponding refined images.

Figure ten. Example refined eye images using the proposed method.
Shows four unlabeled real images of eyes. Also shows five pairs of simulated images, each containing a synthetic and a refined image that depicts the outcome of using the method discussed in the article.

Quantitative Results

Figure eleven shows the improvement using refined data, compared to training with original synthetic data. Two things to note from this figure: (1) Training with refined images is better than training with original synthetic images, and (2) using more synthetic data further improves performance. In Figure twelve, we compare the gaze estimation error with other state-of-the-art methods, and show that improving the realism significantly helps the model generalize on real test data.

Figure eleven. Comparison of training using synthetic and refined images for gaze estimation. Evaluated on real test images.
A plot of percentage of images (y axis) versus distance (x axis), in degrees, from training set accuracy. There are four curves: refined synthetic data four times, refined synthetic data, synthetic data four times, and synthetic data. The refined data curves show an improvement.
Figure twelve. Comparison of different methods for gaze estimation on the MPIIGaze dataset. The first two methods are described in references [2] and [3].
A plot that shows average error in degrees (y axis) for each of four gaze estimation methods. The refined method discussed in this article has the least error.
Figure twelve. Comparison of different methods for gaze estimation on the MPIIGaze dataset. The first two methods are described in references [2] and [3].

Recently there has been a lot of interest in domain adaptation using adversarial training. The image-to-image translation [4] paper by Isola et al. describe a method that learns to change an image from one domain to another, but it needs pixel-wise correspondences. The unpaired image-to-image translation paper [5] discussing relaxing the requirement of pixel-wise correspondence, and follows our strategy of using the generator history to improve the discriminator. The unsupervised image-to-image translation network [6] uses a combination of a GAN and variational auto-encoder to learn the mapping between source and target domains. Costa et al. [7] use ideas from our work to learn to generate images of the eye fundus. Sela et al. [8] use a similar self-regularization approach for facial geometry reconstruction. Lee et al. [9] learn to synthesize an image from key local patches using a discriminator on patches. For more details on the work we describe in this article, see our CVPR paper “Learning from Simulated and Unsupervised Images through Adversarial Training” [10].

References

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[10] A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, R. Webb, Learning from Simulated and Unsupervised Images through Adversarial Training. CVPR, two thousand seventeen