Conditional Image Synthesis with Auxiliary Classifier GANs
In this paper we introduce new methods for the improved training of generative adversarial networks (GANs) for image synthesis. We construct a variant of GANs employing label conditioning that results in resolution image samples exhibiting global coherence. We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models. These analyses demonstrate that high resolution samples provide class information not present in low resolution samples. Across 1000 ImageNet classes, samples are more than twice as discriminable as artificially resized samples. In addition, 84.7% of the classes have samples exhibiting diversity comparable to real ImageNet data.
Characterizing the structure of natural images has been a rich research endeavor. Natural images obey intrinsic invariances and exhibit multi-scale statistical structures that have historically been difficult to quantify (Simoncelli & Olshausen, 2001). Recent advances in machine learning offer an opportunity to substantially improve the quality of image models. Improved image models advance the state-of-the-art in image denoising (Ballé et al., 2015), compression (Toderici et al., 2016), in-painting (van den Oord et al., 2016a), and super-resolution (Ledig et al., 2016). Better models of natural images also improve performance in semi-supervised learning tasks (Kingma et al., 2014; Springenberg, 2015; Odena, 2016; Salimans et al., 2016) and reinforcement learning problems (Blundell et al., 2016).
One method for understanding natural image statistics is to build a system that synthesizes images de novo. There are several promising approaches for building image synthesis models. Variational autoencoders (VAEs) maximize a variational lower bound on the log-likelihood of the training data (Kingma & Welling, 2013; Rezende et al., 2014). VAEs are straightforward to train but introduce potentially restrictive assumptions about the approximate posterior distribution (but see (Rezende & Mohamed, 2015; Kingma et al., 2016)). Autoregressive models dispense with latent variables and directly model the conditional distribution over pixels (van den Oord et al., 2016a, b). These models produce convincing samples but are costly to sample from and do not provide a latent representation. Invertible density estimators transform latent variables directly using a series of parameterized functions constrained to be invertible (Dinh et al., 2016). This technique allows for exact log-likelihood computation and exact inference, but the invertibility constraint is restrictive.
Generative adversarial networks (GANs) offer a distinct and promising approach that focuses on a game-theoretic formulation for training an image synthesis model (Goodfellow et al., 2014). Recent work has shown that GANs can produce convincing image samples on datasets with low variability and low resolution (Denton et al., 2015; Radford et al., 2015). However, GANs struggle to generate globally coherent, high resolution samples - particularly from datasets with high variability. Moreover, a theoretical understanding of GANs is an on-going research topic (Uehara et al., 2016; Mohamed & Lakshminarayanan, 2016).
In this work we demonstrate that that adding more structure to the GAN latent space along with a specialized cost function results in higher quality samples. We exhibit pixel samples from all classes of the ImageNet dataset (Russakovsky et al., 2015) with increased global coherence (Figure 1). Importantly, we demonstrate quantitatively that our high resolution samples are not just naive resizings of low resolution samples. In particular, downsampling our samples to leads to a 50% decrease in visual discriminability. We also introduce a new metric for assessing the variability across image samples and employ this metric to demonstrate that our synthesized images exhibit diversity comparable to training data for a large fraction (84.7%) of ImageNet classes. In more detail, this work is the first to:
Demonstrate an image synthesis model for all 1000 ImageNet classes at a 128x128 spatial resolution (or any spatial resolution - see Section 3).
Measure how much an image synthesis model actually uses its output resolution (Section 4.1).
Measure perceptual variability and ’collapsing’ behavior in a GAN with a fast, easy-to-compute metric (Section 4.2).
Highlight that a high number of classes is what makes ImageNet synthesis difficult for GANs and provide an explicit solution (Section 4.6).
Demonstrate experimentally that GANs that perform well perceptually are not those that memorize a small number of examples (Section 4.3).
A generative adversarial network (GAN) consists of two neural networks trained in opposition to one another. The generator takes as input a random noise vector and outputs an image . The discriminator receives as input either a training image or a synthesized image from the generator and outputs a probability distribution over possible image sources. The discriminator is trained to maximize the log-likelihood it assigns to the correct source:
The generator is trained to minimize the second term in Equation 1.
The basic GAN framework can be augmented using side information. One strategy is to supply both the generator and discriminator with class labels in order to produce class conditional samples (Mirza & Osindero, 2014). Class conditional synthesis can significantly improve the quality of generated samples (van den Oord et al., 2016b). Richer side information such as image captions and bounding box localizations may improve sample quality further (Reed et al., 2016a, b).
Instead of feeding side information to the discriminator, one can task the discriminator with reconstructing side information. This is done by modifying the discriminator to contain an auxiliary decoder network111 Alternatively, one can force the discriminator to work with the joint distribution and train a separate inference network that computes (Dumoulin et al., 2016; Donahue et al., 2016). that outputs the class label for the training data (Odena, 2016; Salimans et al., 2016) or a subset of the latent variables from which the samples are generated (Chen et al., 2016). Forcing a model to perform additional tasks is known to improve performance on the original task (e.g. (Sutskever et al., 2014; Szegedy et al., 2014; Ramsundar et al., 2016)). In addition, an auxiliary decoder could leverage pre-trained discriminators (e.g. image classifiers) for further improving the synthesized images (Nguyen et al., 2016). Motivated by these considerations, we introduce a model that combines both strategies for leveraging side information. That is, the model proposed below is class conditional, but with an auxiliary decoder that is tasked with reconstructing class labels.
We propose a variant of the GAN architecture which we call an auxiliary classifier GAN (or AC-GAN). In the AC-GAN, every generated sample has a corresponding class label, in addition to the noise . uses both to generate images . The discriminator gives both a probability distribution over sources and a probability distribution over the class labels, . The objective function has two parts: the log-likelihood of the correct source, , and the log-likelihood of the correct class, .
is trained to maximize while is trained to maximize . AC-GANs learn a representation for that is independent of class label (e.g. (Kingma et al., 2014)).
Structurally, this model is not tremendously different from existing models. However, this modification to the standard GAN formulation produces excellent results and appears to stabilize training. Moreover, we consider the AC-GAN model to be only part of the technical contributions of this work, along with our proposed methods for measuring the extent to which a model makes use of its given output resolution, methods for measuring perceptual variability of samples from the model, and a thorough experimental analyis of a generative model of images that creates samples from all 1000 ImageNet classes.
Early experiments demonstrated that increasing the number of classes trained on while holding the model fixed decreased the quality of the model outputs. The structure of the AC-GAN model permits separating large datasets into subsets by class and training a generator and discriminator for each subset. All ImageNet experiments are conducted using an ensemble of 100 AC-GANs, each trained on a 10-class split.
We train several AC-GAN models on the ImageNet data set (Russakovsky et al., 2015). Broadly speaking, the architecture of the generator is a series of ‘deconvolution’ layers that transform the noise and class into an image (Odena et al., 2016). We train two variants of the model architecture for generating images at and spatial resolutions. The discriminator is a deep convolutional neural network with a Leaky ReLU nonlinearity (Maas et al., 2013). As mentioned earlier, we find that reducing the variability introduced by all 1000 classes of ImageNet significantly improves the quality of training. We train 100 AC-GAN models – each on images from just 10 classes – for 50000 mini-batches of size 100.
Evaluating the quality of image synthesis models is challenging due to the variety of probabilistic criteria (Theis et al., 2015) and the lack of a perceptually meaningful image similarity metric. Nonetheless, in later sections we attempt to measure the quality of the AC-GAN by building several ad-hoc measures for image sample discriminability and diversity. Our hope is that this work might provide quantitative measures that may be used to aid training and subsequent development of image synthesis models.
4.1 Generating High Resolution Images Improves Discriminability
Building a class-conditional image synthesis model necessitates measuring the extent to which synthesized images appear to belong to the intended class. In particular, we would like to know that a high resolution sample is not just a naive resizing of a low resolution sample. Consider a simple experiment: pretend there exists a model that synthesizes images. One can trivially increase the resolution of synthesized images by performing bilinear interpolation. This would yield higher resolution images, but these images would just be blurry versions of the low resolution images that are not discriminable. Hence, the goal of an image synthesis model is not simply to produce high resolution images, but to produce high resolution images that are more discriminable than low resolution images.
To measure discriminability, we feed synthesized images to a pre-trained Inception network (Szegedy et al., 2015) and report the fraction of the samples for which the Inception network assigned the correct label222 One could also use the Inception score (Salimans et al., 2016), but our method has several advantages: accuracy figures are easier to interpret than exponentiated KL-divergences; accuracy may be assessed for individual classes; accuracy measures whether a class-conditional model generated samples from the intended class. To compute the Inception accuracy, we modified a version of Inception-v3 supplied in https://github.com/openai/improved-gan/. . We calculate this accuracy measure on a series of real and synthesized images which have had their spatial resolution artificially decreased by bilinear interpolation (Figure 2, top panels). Note that as the spatial resolution is decreased, the accuracy decreases - indicating that resulting images contain less class information (Figure 2, scores below top panels). We summarized this finding across all 1000 ImageNet classes for the ImageNet training data (black), a resolution AC-GAN (red) and a resolution AC-GAN (blue) in Figure 2 (bottom, left). The black curve (clipped) provides an upper-bound on the discriminability of real images.
The goal of this analysis is to show that synthesizing higher resolution images leads to increased discriminability. The model achieves an accuracy of 10.1% 2.0% versus 7.0% 2.0% with samples resized to and 5.0% 2.0% with samples resized to . In other words, downsizing the outputs of the AC-GAN to and decreases visual discriminability by 50% and 38% respectively. Furthermore, 84.4% of the ImageNet classes have higher accuracy at than at (Figure 2, bottom left).
We performed the same analysis on an AC-GAN trained to spatial resolution. This model achieved less discriminability than a AC-GAN model. Accuracies from the model plateau at a spatial resolution consistent with previous results. Finally, the resolution model achieves less discriminability at 64 spatial resolution than the model.
To the best of our knowledge, this work is the first that attempts to measure the extent to which an image synthesis model is ‘making use of its given output resolution’, and in fact is the first work to consider the issue at all. We consider this an important contribution, on par with proposing a model that synthesizes images from all 1000 ImageNet classes. We note that the proposed method can be applied to any image synthesis model for which a measure of ‘sample quality’ can be constructed. In fact, this method (broadly defined) can be applied to any type of synthesis model, as long as there is an easily computable notion of sample quality and some method for ‘reducing resolution’. In particular, we expect that a similar procecure can be carried out for audio synthesis.
4.2 Measuring the Diversity of Generated Images
An image synthesis model is not very interesting if it only outputs one image. Indeed, a well-known failure mode of GANs is that the generator will collapse and output a single prototype that maximally fools the discriminator (Goodfellow et al., 2014; Salimans et al., 2016). A class-conditional model of images is not very interesting if it only outputs one image per class. The Inception accuracy can not measure whether a model has collapsed. A model that simply memorized one example from each ImageNet class would do very well by this metric. Thus, we seek a complementary metric to explicitly evaluate the intra-class perceptual diversity of samples generated by the AC-GAN.
Several methods exist for quantitatively evaluating image similarity by attempting to predict human perceptual similarity judgements. The most successful of these is multi-scale structural similarity (MS-SSIM) (Wang et al., 2004b; Ma et al., 2016). MS-SSIM is a multi-scale variant of a well-characterized perceptual similarity metric that attempts to discount aspects of an image that are not important for human perception (Wang et al., 2004a). MS-SSIM values range between 0.0 and 1.0; higher MS-SSIM values correspond to perceptually more similar images.
As a proxy for image diversity, we measure the MS-SSIM scores between 100 randomly chosen pairs of images within a given class. Samples from classes that have higher diversity result in lower mean MS-SSIM scores (Figure 3, left columns); samples from classes with lower diversity have higher mean MS-SSIM scores (Figure 3, right columns). Training images from the ImageNet training data contain a variety of mean MS-SSIM scores across the classes indicating the variability of image diversity in ImageNet classes (Figure 4, x-axis). Note that the highest mean MS-SSIM score (indicating the least variability) is 0.25 for the training data.
We calculate the mean MS-SSIM score for all 1000 ImageNet classes generated by the AC-GAN model. We track this value during training to identify whether the generator has collapsed (Figure 5, red curve). We also employ this metric to compare the diversity of the training images to the samples from the GAN model after training has completed. Figure 4 plots the mean MS-SSIM values for image samples and training data broken up by class. The blue line is the line of equality. Out of the 1000 classes, we find that 847 have mean sample MS-SSIM scores below that of the maximum MS-SSIM for the training data. In other words, 84.7% of classes have sample variability that exceeds that of the least variable class from the ImageNet training data.
There are two points related to the MS-SSIM metric and our use of it that merit extra attention. The first point is that we are ‘abusing’ the metric: it was originally intended to be used for measuring the quality of image compression algorithms using a reference ‘original image’. We instead use it on two potentially unrelated images. We believe that this is acceptable for the following reasons: First: visual inspection seems to indicate that the metric makes sense - pairs with higher MS-SSIM do seem more similar than pairs with lower MS-SSIM. Second: we restrict comparisons to images synthesized using the same class label. This restricts use of MS-SSIM to situations more similar to those in which it is typically used (it is not important which image is the reference). Third: the metric is not ‘saturated’ for our use-case. If most scores were around 0, then we would be more concerned about the applicability of MS-SSIM. Finally: The fact that training data achieves more variability by this metric (as expected) is itself evidence that the metric is working as intended.
The second point is that the MS-SSIM metric is not intended as a proxy for the entropy of the generator distribution in pixel space, but as a measure of perceptual diversity of the outputs. The entropy of the generator output distribution is hard to compute and pairwise MS-SSIM scores would not be a good proxy. Even if it were easy to compute, we argue that it would still be useful to have a separate measure of perceptual diversity. To see why, consider that the generator entropy will be sensitive to trivial changes in contrast as well as changes in the semantic content of the outputs. In many applications, we don’t care about this contribution to the entropy, and it is useful to consider measures that attempt to ignore changes to an image that we consider ‘perceptually meaningless’, hence the use of MS-SSIM.
4.3 Generated Images are both Diverse and Discriminable
We have presented quantitative metrics demonstrating that AC-GAN samples may be diverse and discriminable but we have yet to examine how these metrics interact. Figure 6 shows the joint distribution of Inception accuracies and MS-SSIM scores across all classes. Inception accuracy and MS-SSIM are anti-correlated (). In fact, 74% of the classes with low diversity (MS-SSIM ) contain Inception accuracies . Conversely, 78% of classes with high diversity (MS-SSIM ) have Inception accuracies that exceed 1%. In comparison, the Inception-v3 model achieves 78.8% accuracy on average across all 1000 classes (Szegedy et al., 2015). A fraction of the classes AC-GAN samples reach this level of accuracy. This indicates opportunity for future image synthesis models.
These results suggest that GANs that drop modes are most likely to produce low quality images. This stands in contrast to a popular hypothesis about GANs, which is that they achieve high sample quality at the expense of variability. We hope that these findings can help structure further investigation into the reasons for differing sample quality between GANs and other image synthesis models.
4.4 Comparison to Previous Results
Previous quantitative results for image synthesis models trained on ImageNet are reported in terms of log-likelihood (van den Oord et al., 2016a, b). Log-likelihood is a coarse and potentially inaccurate measure of sample quality (Theis et al., 2015). Instead we compare with previous state-of-the-art results on CIFAR-10 using a lower spatial resolution (). Following the procedure in (Salimans et al., 2016), we compute the Inception score333 The Inception score is given by where is a particular image, is the conditional output distribution over the classes in a pre-trained Inception network (Szegedy et al., 2014) given , and is the marginal distribution over the classes. for 50000 samples from an AC-GAN with resolution (), split into 10 groups at random. We also compute the Inception score for 25000 extra samples, split into 5 groups at random. We select the best model based on the first score and report the second score. Performing a grid search across 27 hyperparameter configurations, we are able to achieve a score of 8.25 0.07 compared to state of the art 8.09 0.07 (Salimans et al., 2016). Moreover, we accomplish this without employing any of the new techniques introduced in that work (i.e. virtual batch normalization, minibatch discrimination, and label smoothing).
4.5 Searching for Signatures of Overfitting
One possibility that must be investigated is that the AC-GAN has overfit on the training data. As a first check that the network does not memorize the training data, we identify the nearest neighbors of image samples in the training data measured by L1 distance in pixel space (Figure 8). The nearest neighbors from the training data do not resemble the corresponding samples. This provides evidence that the AC-GAN is not merely memorizing the training data.
A more sophisticated method for understanding the degree of overfitting in a model is to explore that model’s latent space by interpolation. In an overfit model one might observe discrete transitions in the interpolated images and regions in latent space that do not correspond to meaningful images (Bengio et al., 2012; Radford et al., 2015; Dinh et al., 2016). Figure 9 (Top) highlights interpolations in the latent space between several image samples. Notably, the generator learned that certain combinations of dimensions correspond to semantically meaningful features (e.g. size of the arch, length of a bird’s beak) and there are no discrete transitions or ‘holes’ in the latent space.
A second method for exploring the latent space of the AC-GAN is to exploit the structure of the model. The AC-GAN factorizes its representation into class information and a class-independent latent representation . Sampling the AC-GAN with fixed but altering the class label corresponds to generating samples with the same ‘style’ across multiple classes (Kingma et al., 2014). Figure 9 (Bottom) shows samples from 8 bird classes. Elements of the same row have the same . Although the class changes for each column, elements of the global structure (e.g. position, layout, background) are preserved, indicating that AC-GAN can represent certain types of ‘compositionality’.
4.6 Measuring the Effect of Class Splits on Image Sample Quality.
Class conditional image synthesis affords the opportunity to divide up a dataset based on image label. In our final model we divide 1000 ImageNet classes across 100 AC-GAN models. In this section we describe experiments that highlight the benefit of cutting down the diversity of classes for training an AC-GAN. We employed an ordering of the labels and divided it into contiguous groups of 10. This ordering can be seen in the following section, where we display samples from all 1000 classes. Two aspects of the split merit discussion: the number of classes per split and the intra-split diversity. We find that training a fixed model on more classes harms the model’s ability to produce compelling samples (Figure 10). Performance on larger splits can be improved by giving the model more parameters. However, using a small split is not sufficient to achieve good performance. We were unable to train a GAN (Goodfellow et al., 2014) to converge reliably even for a split size of 1. This raises the question of whether it is easier to train a model on a diverse set of classes than on a similar set of classes: We were unable to find conclusive evidence that the selection of classes in a split significantly affects sample quality.
We don’t have a hypothesis about what causes this sensitivity to class count that is well-supported experimentally. We can only note that, since the failure case that occurs when the class count is increased is ‘generator collapse’, it seems plausible that general methods for addressing ‘generator collapse’ could also address this sensitivity.
4.7 Samples from all 1000 ImageNet Classes
We also generate 10 samples from each of the 1000 ImageNet classes, hosted here. As far as we are aware, no other image synthesis work has included a similar analysis.
This work introduced the AC-GAN architecture and demonstrated that AC-GANs can generate globally coherent ImageNet samples. We provided a new quantitative metric for image discriminability as a function of spatial resolution. Using this metric we demonstrated that our samples are more discriminable than those from a model that generates lower resolution images and performs a naive resize operation. We also analyzed the diversity of our samples with respect to the training data and provided some evidence that the image samples from the majority of classes are comparable in diversity to ImageNet data.
Several directions exist for building upon this work. Much work needs to be done to improve the visual discriminability of the resolution model. Although some synthesized image classes exhibit high Inception accuracies, the average Inception accuracy of the model () is still far below real training data at 81%. One immediate opportunity for addressing this is to augment the discriminator with a pre-trained model to perform additional supervised tasks (e.g. image segmentation, (Ronneberger et al., 2015)).
Improving the reliability of GAN training is an ongoing research topic. Only 84.7% of the ImageNet classes exhibited diversity comparable to real training data. Training stability was vastly aided by dividing up 1000 ImageNet classes across 100 AC-GAN models. Building a single model that could generate samples from all 1000 classes would be an important step forward.
Image synthesis models provide a unique opportunity for performing semi-supervised learning: these models build a rich prior over natural image statistics that can be leveraged by classifiers to improve predictions on datasets for which few labels exist. The AC-GAN model can perform semi-supervised learning by ignoring the component of the loss arising from class labels when a label is unavailable for a given training image. Interestingly, prior work suggests that achieving good sample quality might be independent of success in semi-supervised learning (Salimans et al., 2016).
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Appendix A Hyperparameters
|Optimizer||Adam (, , )|
|Leaky ReLU slope||0.2|
|Weight, bias initialization||Isotropic gaussian (, ), Constant()|
|Generator Optimizer||Adam (, , )|
|Discriminator Optimizer||Adam (, , )|
|Leaky ReLU slope||0.2|
|Activation noise standard deviation|
|Weight, bias initialization||Isotropic gaussian (, ), Constant()|