Deep-Learning-with-PyTorch

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28 CHAPTER 2 Pretrained networksWhile this scenario is a bit farcical, the underlying technology is sound and willlikely have a profound impact on the perceived veracity of digital data in the years tocome. The entire concept of “photographic evidence” is likely to become entirely suspect,given how easy it will be to automate the production of convincing, yet fake,images and video. The only key ingredient is data. Let’s see how this process works.2.2.1 The GAN gameIn the context of deep learning, what we’ve just described is known as the GAN game,where two networks, one acting as the painter and the other as the art historian, competeto outsmart each other at creating and detecting forgeries. GAN stands for generativeadversarial network, where generative means something is being created (in thiscase, fake masterpieces), adversarial means the two networks are competing to outsmartthe other, and well, network is pretty obvious. These networks are one of themost original outcomes of recent deep learning research.Remember that our overarching goal is to produce synthetic examples of a class ofimages that cannot be recognized as fake. When mixed in with legitimate examples, askilled examiner would have trouble determining which ones are real and which areour forgeries.The generator network takes the role of the painter in our scenario, tasked with producingrealistic-looking images, starting from an arbitrary input. The discriminator networkis the amoral art inspector, needing to tell whether a given image was fabricatedby the generator or belongs in a set of real images. This two-network design is atypicalfor most deep learning architectures but, when used to implement a GAN game, canlead to incredible results.Figure 2.5 shows a rough picture of what’s going on. The end goal for the generatoris to fool the discriminator into mixing up real and fake images. The end goal for thediscriminator is to find out when it’s being tricked, but it also helps inform the generatorabout the identifiable mistakes in the generated images. At the start, the generatorproduces confused, three-eyed monsters that look nothing like a Rembrandt portrait.The discriminator is easily able to distinguish the muddled messes from the real paintings.As training progresses, information flows back from the discriminator, and thegenerator uses it to improve. By the end of training, the generator is able to produceconvincing fakes, and the discriminator no longer is able to tell which is which.Note that “Discriminator wins” or “Generator wins” shouldn’t be taken literally—there’s no explicit tournament between the two. However, both networks are trainedbased on the outcome of the other network, which drives the optimization of theparameters of each network.This technique has proven itself able to lead to generators that produce realisticimages from nothing but noise and a conditioning signal, like an attribute (for example,for faces: young, female, glasses on) or another image. In other words, a welltrainedgenerator learns a plausible model for generating images that look real evenwhen examined by humans.
A pretrained model that fakes it until it makes it29GENERATORREALIMAGESDISCRIMINATORREAL!FAKE!GETS BETtERAT MAKINGSTUFf UPGENERATEDIMAGEGETS BETtERAT NOT BEINGFOoLEDDISCRIMINATOR WINSLOoKSLEGIT!GENERATOR WINSFigure 2.5Concept of a GAN game2.2.2 CycleGANAn interesting evolution of this concept is the CycleGAN. A CycleGAN can turnimages of one domain into images of another domain (and back), without the needfor us to explicitly provide matching pairs in the training set.In figure 2.6, we have a CycleGAN workflow for the task of turning a photo of ahorse into a zebra, and vice versa. Note that there are two separate generator networks,as well as two distinct discriminators.INPUTG A2BD BD AG B2AREALZEBRA!REALHORSE!...SAME PROCESs STARTINGFROM ZEBRA...Figure 2.6A CycleGAN trained to the point that it can fool both discriminator networks
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Eli StevensLuca AntigaThomas Viehma
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Deep Learningwith PyTorchELI STEVEN
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To my wife (this book would not hav
Page 8 and 9: viCONTENTS23Pretrained networks 162
Page 10 and 11: viiiCONTENTS675.4 Down along the gr
Page 12 and 13: xCONTENTS10119.5 Conclusion 2529.6
Page 14 and 15: xiiCONTENTS1413.3 Semantic segmenta
Page 17 and 18: forewordWhen we started the PyTorch
Page 19 and 20: prefaceAs kids in the 1980s, taking
Page 21 and 22: acknowledgmentsWe are deeply indebt
Page 23 and 24: about this bookThis book has the ai
Page 25 and 26: ABOUT THIS BOOKxxiiiPART 2In part 2
Page 27 and 28: ABOUT THIS BOOKxxvMany of the code
Page 29 and 30: about the authorsEli Stevens has sp
Page 31: Part 1Core PyTorchWelcome to the fi
Page 34 and 35: 4 CHAPTER 1 Introducing deep learni
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78 CHAPTER 4 Real-world data repres
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100 CHAPTER 4 Real-world data repre
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102 CHAPTER 4 Real-world data repre
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104 CHAPTER 5 The mechanics of lear
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142 CHAPTER 6 Using a neural networ
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Telling birdsfrom airplanes:Learnin
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166 CHAPTER 7 Telling birds from ai
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194 CHAPTER 8 Using convolutions to
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Part 2Learning from imagesin the re
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236 CHAPTER 9 Using PyTorch to figh
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Combining data sourcesinto a unifie
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256 CHAPTER 10 Combining data sourc
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280 CHAPTER 11 Training a classific
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Improving trainingwith metrics anda
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320 CHAPTER 12 Improving training w
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358 CHAPTER 13 Using segmentation t
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End-to-endnodule analysis,and where
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406 CHAPTER 14 End-to-end nodule an
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Deploying to productionThis chapter
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Serving PyTorch models447The API ca
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Serving PyTorch models449When using
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Serving PyTorch models451IMPLEMENTA
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Serving PyTorch models453Listing 15
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Exporting models45515.2 Exporting m
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Exporting models457But then all we
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Interacting with the PyTorch JIT459
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LibTorch: PyTorch in C++465Listing
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LibTorch: PyTorch in C++467Our tens
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LibTorch: PyTorch in C++469We’ll
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LibTorch: PyTorch in C++471...model
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Going mobile473Then, we need to inc
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Going mobile475and full of copying
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Summary47715.7 ConclusionThis concl
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480INDEXbool tensors 51Boolean inde
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482INDEXdata loading (continued)con
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484INDEXimage recognition (continue
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486INDEXneural networks (continued)
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488INDEXResNetGenerator module470-4
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490INDEXVval_loss tensor 138val_neg
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PYTHON/DATA SCIENCEDeep Learning wi
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