Generative Adversarial Networks (GANs)
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Generative Adversarial Networks (GANs)
https://www.theverge.com/2019/3/19/18272602/ai-art-generation-gan-nvidia-doodle-landscapes
Colorizing (not Restoring)
https://blog.floydhub.com/colorizing-and-restoring-old-images-with-deep-learning/
https://news.developer.nvidia.com/generating-and-editing-high-resolution-synthetic-images-with-gans/?ncid=--43604
https://github.com/Aggregate-Intellect/awesome-does-not-existPeople• This Person Does Not Exist• Which Face is Real
Animal• This Cat Does Not Exist• These Cats Do Not Exist
Text & Documents• Was this poem written by a
human or a computer?• This Resume Does Not Exist
Objects• This Car Does Not Exist• (Video) These Cars Do Not Exist• (Video) This Ramen Does Not Exist
Rooms• This Rental Does Not Exist
Entities• This Startup Does Not Exist
Cartoon/Anime• This Waifu Does Not Exist• These Waifus Do Not Exist
GANs
• Two deep neural networks working against each other (adversarial)• A generator creates something new• A discriminator tries to identify generated data vs real data
• GANs have been applied in many domains• Computer vision (classification, regression, etc.)• Audio generation• Speech recognition• NLP (generation and recognition)
Generative Adversarial Networks, Goodfellow et al. (2014)
GANs
Real Data
Fake Data(Generated)
Discriminator
GeneratorGenerationParameters(Template)
Prediction
GANs
Real Data
Fake Data(Generated)
Discriminator
GeneratorGenerationParameters(Template)
Prediction
Classification (real or fake)
What kind of network would you use?
GANs
Real Data
Fake Data(Generated)
Discriminator
GeneratorGenerationParameters(Template)
Prediction
Classification (real or fake)
What kind of network would you use?
Regression (generate features that fool the discriminator)
What kind of network would you use?
GANs
Real Data
Fake Data(Generated)
Discriminator(ResNet-34)
Generator(U-Net)
GenerationParameters(Template)
Prediction
Classification (real or fake)
What kind of network would you use?
Regression (generate features that fool the discriminator)
What kind of network would you use?
Example architectures assume we are working with image data.
Fastai Method
Freeze the generator and train the critic for one step:• grab one batch of real images• generating one batch of fake images• compute the critic’s loss on all real and fake images• update the critic’s parameters
Freeze the critic and train the generator for one step:• generating one batch of fake images• use the critic to evaluate the fake images• compute the generator’s loss based on its ability to fool the critic• update the generator’s parameters
Related Technique: Autoencoder
https://blog.keras.io/building-autoencoders-in-keras.html
Autoencoding is a data compression algorithm where the compression and decompression functions are:
1) data-specific2) lossy3) learned automatically from examples rather than
engineered by a human.
Practical applications of autoencoders: 1) data denoising2) dimensionality reduction for data visualization
With appropriate dimensionality and sparsity constraints, autoencoders can learn data projections that are more interesting than PCA or other basic techniques.
Related Technique: Variational Autoencoders
http://kvfrans.com/variational-autoencoders-explained/
In [1]: %reload_ext autoreload%autoreload 2%matplotlib inline
In [2]: from fastai.vision import *from fastai.vision.gan import *
In [4]: data_path = untar_data(URLs.LSUN_BEDROOMS)
data_transforms = get_transforms()
image_size = 64batch_size = 128
def get_data(path, bs, size):return (GANItemList.from_folder(path)#, noise_sz=100)
.no_split()
.label_from_func(noop)
.transform(tfms=[[crop_pad(size=size, row_pct=(0,1), col_pct=(0,1))], []], size=size, tfm_y=True)
.databunch(bs=bs)
.normalize(stats = [torch.tensor([0.5,0.5,0.5]), torch.tensor([0.5,0.5,0.5])], do_x=False, do_y=True))
data = get_data(data_path, batch_size, image_size)
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In [5]: data.show_batch()
In [6]: generator = basic_generator(in_size=image_size, n_channels=3, n_extra_layers=1)critic = basic_critic (in_size=image_size, n_channels=3, n_extra_layers=1)
In [7]: learn = GANLearner.wgan(data, generator, critic, switch_eval=False,opt_func = partial(optim.Adam, betas = (0.,0.99)), wd=0.)
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In [8]: learn.fit(20, 3e-4)
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Total time: 44:42
epoch train_loss gen_loss disc_loss
1 -0.605838 0.424219 -0.800274
2 -0.581525 0.419157 -0.776750
3 -0.505078 0.329789 -0.677572
4 -0.453455 0.324825 -0.608423
5 -0.444416 0.205033 -0.545488
6 -0.353311 0.186529 -0.470391
7 -0.284996 0.151906 -0.379780
8 -0.271385 0.134970 -0.357321
9 -0.241809 0.123559 -0.317124
10 -0.231374 0.123763 -0.298770
11 -0.201024 0.109258 -0.283154
12 -0.188730 0.136541 -0.267419
13 -0.193091 0.096157 -0.247904
14 -0.165972 0.114151 -0.240640
15 -0.179664 0.085967 -0.236525
16 -0.186926 0.062920 -0.226390
17 -0.153478 0.088798 -0.209249
18 -0.176428 0.055510 -0.212063
19 -0.141382 0.089210 -0.192962
20 -0.164651 0.052121 -0.201119
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In [9]: learn.gan_trainer.switch(gen_mode=True)learn.show_results(ds_type=DatasetType.Train)
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