In this chapter, we introduce some of the concepts that enable deep learning models to seemingly create art, an idea that may be paradoxical to some. The University of California, Berkeley, philosopher Alva Noë, for one, opined, “Art can help us frame a better picture of our human nature.”1 If this is true, how can machines create art? Or put differently, are the creations that emerge from these machines, in fact, art? Another interpretation—and one we like best—is that these creations are indeed art and that programmers are artists wielding deep learning models as brushes. We’re not the only ones who view these works as bona fide artistry: generative adversarial network (GAN)-produced paintings have been snapped up to the tune of $400,000 a pop.2
1. Noë, A. (2015, October 5). What art unveils. The New York Times.
2. Cohn, G. (2018, October 25). AI art at Christie’s sells for $432,500. The New York Times.
Over the course of this chapter, we cover the high-level concepts behind GANs, and you will see examples of the novel visual works they can produce. We will draw a link between the latent spaces associated with GANs and the word-vector spaces of Chapter 2. And we will cover a deep learning model that can be used as an automated tool for dramatically improving the quality of photos. But before we do any of that, let’s grab a drink . . .
Below Google’s offices in Montreal sits a bar called Les 3 Brasseurs, a moniker that translates from French to “The 3 Brewers.” It was at this watering hole in 2014, while a PhD student in Yoshua Bengio’s renowned lab (Figure 1.10), that Ian Goodfellow conceived of an algorithm for fabricating realistic-looking images,3 a technique that Yann LeCun (Figure 1.9) has hailed as the “most important” recent breakthrough in deep learning.4
3. Giles, M. (2018, February 21). The GANfather: The man who’s given machines the gift of imagination. MIT Technology Review.
4. LeCun, Y. (2016, July 28). Quora.
Goodfellow’s friends described to him a generative model they were working on, that is, a computational model that aims to produce something novel, be it a quote in the style of Shakespeare, a musical melody, or a work of abstract art. In their particular case, the friends were attempting to design a model that could generate photorealistic images such as portraits of human faces. For this to work well via the traditional machine learning approach (Figure 1.12), the engineers designing the model would need to not only catalog and approximate the critical individual features of faces like eyes, noses, and mouths, but also accurately estimate how these features should be arranged relative to each other. Thus far, their results had been underwhelming. The generated faces tended to be excessively blurry, or they tended to be missing essential elements like the nose or the ears.
Perhaps with his creativity heightened by a pint of beer or two,5 Goodfellow proposed a revolutionary idea: a deep learning model in which two artificial neural networks (ANNs) act against each other competitively as adversaries. As illustrated in Figure 3.1, one of these deep ANNs would be programmed to produce forgeries while the other would be programmed to act as a detective and distinguish the fakes from real images (which would be provided separately). These adversarial deep learning networks would play off one another: As the generator became better at producing fakes, the discriminator would need to become better at identifying them, and so the generator would need to produce even more compelling counterfeits, and so on. This virtuous cycle would eventually lead to convincing novel images in the style of the real training images, be they of faces or otherwise. Best of all, Goodfellow’s approach would circumnavigate the need to program features into the generative model manually. As we expounded with respect to machine vision (Chapter 1) and natural language processing (Chapter 2), deep learning would sort out the model’s features automatically.
5. Jarosz, A., et al. (2012). Uncorking the muse: Alcohol intoxication facilitates creative problem solving. Consciousness and Cognition, 21, 487–93.
Goodfellow’s friends were doubtful his imaginative approach would work. So, when he arrived home and found his girlfriend asleep, he worked late to architect his dual-ANN design. It worked the first time, and the astounding deep learning family of generative adversarial networks was born!
That same year, Goodfellow and his colleagues revealed GANs to the world at the prestigious Neural Information Processing Systems (NIPS) conference.6 Some of their results are shown in Figure 3.2. Their GAN produced these novel images by being trained on (a) handwritten digits;7 (b) photos of human faces;8 and (c) and (d) photos from across ten diverse classes (e.g., planes, cars, dogs).9 The results in (c) are markedly less crisp than in (d), because the GAN that produced the latter featured neuron layers specialized for machine vision called convolutional layers,10 whereas the GAN that produced the former used a more general layer type only.11
6. Goodfellow, I., et al. (2014). Generative adversarial networks. arXiv:1406.2661.
9. The CIFAR-10 dataset, which is named after the Canadian Institute for Advanced Research that supported its creation.
Following on from Goodfellow’s lead, a research team led by the American machine learning engineer Alec Radford determined architectural constraints for GANs that guide considerably more realistic image creation. Some examples of portraits of fake humans that were produced by their deep convolutional GANs12 are provided in Figure 3.3. In their paper, Radford and his teammates cleverly demonstrated interpolation through, and arithmetic with, the latent space associated with GANs. Let’s start off by explaining what latent space is before moving on to latent-space interpolation and arithmetic.
12. Radford, A., et al. (2016). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434v2.
The latent-space cartoon in Figure 3.4 may be reminiscent of the word-vector space cartoon in Figure 2.6. As it happens, there are three major similarities between latent spaces and vector spaces. First, while the cartoon is only three-dimensional for simplicity and comprehensibility, latent spaces are n-dimensional spaces, usually in the order of hundreds of dimensions. The latent space of the GAN you’ll later architect yourself in Chapter 12, for example, will have n = 100 dimensions. Second, the closer two points are in the latent space, the more similar the images that those points represent. And third, movement through the latent space in any particular direction can correspond to a gradual change in a concept being represented, such as age or gender for the case of photorealistic faces.
By picking two points far away from each other along some n-dimensional axis representing age, interpolating between them, and sampling points from the interpolated line, we could find what appears to be the same (fabricated) man gradually appearing to be older and older.13 In our latent-space cartoon (Figure 3.4), we represent such an “age” axis in purple. To observe interpolation through an authentic GAN latent space, we recommend scanning through Radford and colleagues’ paper for, as an example, smooth rotations of the “photo angle” of synthetic bedrooms. At the time of writing, the state of the art in GANs can be viewed at
bit.ly/InterpCeleb. This video, produced by researchers at the graphics-card manufacturer Nvidia, provides a breathtaking interpolation through high-quality portrait “photographs” of ersatz celebrities.14,15
13. A technical aside: As is the case with vector spaces, this “age” axis (or any other direction within latent space that represents some meaningful attribute) may be orthogonal to all of the n dimensions that constitute the axes of the n-dimensional space. We discuss this further in Chapter 11.
14. Karras, T., et al. (2018). Progressive growing of GANs for improved quality, stability, and variation. Proceedings of the International Conference on Learning Representations.
Moving a step further with what you’ve learned, you could now perform arithmetic with images sampled from a GAN’s latent space. When sampling a point within the latent space, that point can be represented by the coordinates of its location—the resulting vector is analogous to the word vectors described in Chapter 2. As with word vectors, you can perform arithmetic with these vectors and move through the latent space in a semantic way. Figure 3.3 showcases an instance of latent-space arithmetic from Radford and his coworkers. Starting with a point in their GAN’s latent space that represents a man with glasses, subtracting a point that represents a man without glasses, and adding a point representing a woman without glasses, the resulting point exists in the latent space near to images that represent women with glasses. Our cartoon in Figure 3.4 illustrates how the relationships between meaning in latent space are stored (again, akin to the way they are in word-vector space), thereby facilitating arithmetic on points in latent space.
One of the more magical applications of GANs is style transfer. Zhu, Park, and their coworkers from the Berkeley Artificial Intelligence Research (BAIR) Lab introduced a new flavor of GAN16 that enables stunning examples of this, as shown in Figure 3.5. Alexei Efros, one of the paper’s coauthors, took photos while on holiday in France and the researchers employed their CycleGAN to transfer these photos into the style of the Impressionist painter Claude Monet, the nineteenth-century Dutch artist Vincent Van Gogh, and the Japanese Ukiyo-e genre, among others. If you navigate to
bit.ly/cycleGAN, you’ll be delighted to discover instances of the inverse (Monet paintings converted into photorealistic images), as well as:
Summer scenes converted into wintry ones, and vice versa
Baskets of apples converted into baskets of oranges, and vice versa
Flat, low-quality photos converted into what appear to be ones captured by high-end (single-lens reflex) cameras
A video of a horse running in a field converted into a zebra
A video of a drive taken during the day converted into a nighttime one
16. Called “CycleGANs” because they retain image consistency over multiple cycles of network training. Zhu, J.-Y., et al. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv:1703.10593.
Another GAN application out of Alexei Efros’s BAIR lab, and one that you can amuse yourself with straightaway, is pix2pix.17 If you make your way to
bit.ly/pix2pixDemo, you can interactively translate images from one type to another. Using the edges2cats tool, for example, we sketched the three-eyed cat in the left-hand panel of Figure 3.6 to generate the photorealistic(-ish) mutant kitty in the right-hand panel. As it takes your fancy, you are also welcome to convert your own creative visions of felines, shoes, handbags, and building façades into photorealistic analogs within your browser. The authors of the pix2pix paper call their approach a conditional GAN (cGAN for short) because the generative adversarial network produces an output that is conditional on the particular input provided to it.
17. Isola, P., et al. (2017). Image-to-image translation with conditional adversarial networks. arXiv:1611.07004.
To round out this chapter, we’d like you to take a gander at the truly photorealistic high-resolution images in Figure 3.7. These images were generated by StackGAN,18 an approach that stacks two GANs on top of each other. The first GAN in the architecture is configured to produce a rough, low-resolution image with the general shape and colors of the relevant objects in place. This is then supplied to the second GAN as its input, where the forged “photo” is refined by fixing up imperfections and adding considerable detail. The StackGAN is a cGAN like the pix2pix network in the preceding section; however, the image output is conditioned on text input instead of an image.
18. Zhang, H., et al. (2017). StackGAN: Text to photo-realistic image synthesis with stacked generative adversarial networks. arXiv:1612.03242v2.
Since the advent of digital camera technology, image processing (both on-device and postprocessing) has become a staple in most (if not all) photographers’ workflows. This ranges from simple on-device processing, such as increasing saturation and sharpness immediately after capture, to complex editing of raw image files in software applications like Adobe Photoshop and Lightroom.
Machine learning has been used extensively in on-device processing, where the camera manufacturer would like the image that the consumer sees to be vibrant and pleasing to the eye with minimal user effort. Some examples of this are:
Early face-recognition algorithms in point-and-shoot cameras, which optimize the exposure and focus for faces or even selectively fire the shutter when they recognize that the subject is smiling (as in Figure 1.13)
Scene-detection algorithms that adjust the exposure settings to capture the whiteness of snow or activate the flash for nighttime photos
In the postprocessing arena a variety of automatic tools exists, although generally photographers who are taking the time to postprocess images are investing considerable time and domain-specific knowledge into color and exposure correction, denoising, sharpening, tone mapping, and touching up (to name just a few of the corrections that may be applied).
Historically, these corrections have been difficult to execute programmatically, because, for example, denoising might need to be applied selectively to different images and even different parts of the same image. This is exactly the kind of intelligent application that deep learning is poised to excel at.
In a 2018 paper from Chen Chen and his collaborators at Intel Labs,19 deep learning was applied to the enhancement of images that were taken in near total darkness, with astonishing results (Figure 3.8). In a phrase, their deep learning model involves convolutional layers organized into the innovative U-Net20 architecture (which we break down for you in Chapter 10). The authors generated a custom dataset for training this model: the See-in-the-Dark dataset consists of 5,094 raw images of very dark scenes using a short-exposure image21 with a corresponding long-exposure image (using a tripod for stability) of the same scene. The exposure times on the long-exposure images were 100 to 300 times those of the short-exposure training images, with actual exposure times in the range of 10 to 30 seconds. As demonstrated in Figure 3.8, the deep-learning-based image-processing pipeline of U-Net (right panel) far outperforms the results of the traditional pipeline (center panel). There are, however, limitations as yet:
The model is not fast enough to perform this correction in real time (and certainly not on-device); however, runtime optimization could help here.
A dedicated network must be trained for different camera-models and sensors, whereas a more generalized and camera-model-agnostic approach would be favorable.
While the results far exceed the capabilities of traditional pipelines, there are still some artifacts present in the enhanced photos that could stand to be improved.
The dataset is limited to selected static scenes and needs to be expanded to other subjects (most notably, humans).
19. Chen, C., et al. (2018) Learning to see in the dark. arXiv:1805.01934.
20. Ronneberger et al. (2015) U-Net: Convolutional networks for biomedical image segmentation. arXiv: 1505.04597.
21. That is, a short enough exposure time to enable practical handheld capture without motion blur but that renders images too dark to be useful.
Limitations aside, this work nevertheless provides a beguiling peek into how deep learning can adaptively correct images in photograph postprocessing pipelines with a level of sophistication not before seen from machines.
In this chapter, we introduced GANs and conveyed that this deep learning approach encodes exceptionally sophisticated representations within their latent spaces. These rich visual representations enable GANs to create novel images with particular, granular artistic styles. The outputs of GANs aren’t purely aesthetic; they can be practical, too. They can, as examples, simulate data for training autonomous vehicles, hurry the pace of prototyping in the fields of fashion and architecture, and substantially augment the capacities of creative humans.22
22. Carter, S., and Nielsen, M. (2017, December 4). Using artificial intelligence to augment human intelligence. Distill.
In Chapter 12, after we get all of the prerequisite deep learning theory out of the way, you’ll construct a GAN yourself to imitate sketches from the Quick, Draw! dataset (introduced at the end of Chapter 1). Take a gander at Figure 3.9 for a preview of what you’ll be able to do.