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Chapter 6The preceding images were the initial attempts by the GAN. As it learned throughthe following 5000 epochs, the quality of digits generated improved manyfold:The complete code is available in DCGAN.ipynb in the GitHub repo. We can takethe concepts discussed here and apply them to images in other domains. One of theinteresting work on images was reported in the paper, Unsupervised RepresentationLearning with Deep Convolutional Generative Adversarial Networks, Alec Radford, LukeMetz, Soumith Chintala, 2015. Quoting the abstract:"In recent years, supervised learning with convolutional networks (CNNs) hasseen huge adoption in computer vision applications. Comparatively, unsupervisedlearning with CNNs has received less attention. In this work we hope to help bridgethe gap between the success of CNNs for supervised learning and unsupervisedlearning. We introduce a class of CNNs called deep convolutional generativeadversarial networks (DCGANs), that have certain architectural constraints,and demonstrate that they are a strong candidate for unsupervised learning.Training on various image datasets, we show convincing evidence that our deepconvolutional adversarial pair learns a hierarchy of representations from objectparts to scenes in both the generator and discriminator. Additionally, we use thelearned features for novel tasks - demonstrating their applicability as general imagerepresentations."(Radford et al., 2015).[ 207 ]
Generative Adversarial NetworksFollowing are some of the interesting results of applying DCGANs to a celebrityimage dataset:[ 208 ]
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- Page 228 and 229: [ 193 ]Chapter 6Eventually, we reac
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- Page 234 and 235: Chapter 6Figure 1: Visualizing the
- Page 236 and 237: Chapter 6The resultant generator mo
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- Page 240 and 241: Chapter 6def train(self, epochs, ba
- Page 244 and 245: Chapter 6Another interesting paper
- Page 246 and 247: Chapter 6To elaborate, let us say t
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- Page 252 and 253: Chapter 6Bedrooms: Generated bedroo
- Page 254 and 255: Chapter 6The images need to be norm
- Page 256 and 257: Chapter 6initializer = tf.random_no
- Page 258 and 259: Cool, right? Now we can define the
- Page 260 and 261: Chapter 6d_loss = (dA_loss + dB_los
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- Page 264: 6. Ledig, Christian, et al. Photo-R
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- Page 277 and 278: Word Embeddingsgensim also provides
- Page 279 and 280: Word EmbeddingsSpecifically, we wil
- Page 281 and 282: Word EmbeddingsWe will also convert
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- Page 285 and 286: Word Embeddingsx = self.embedding(x
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Generative Adversarial Networks
Following are some of the interesting results of applying DCGANs to a celebrity
image dataset:
[ 208 ]
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