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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Alec Radford, Luke Metz, Soumith Chintala

All images in this paper are generated by a neural network. They are NOT REAL.

Full paper here: http://arxiv.org/abs/1511.06434

###Other implementations of DCGAN

##Summary of DCGAN We

Bedrooms after 5 epochs

Generated bedrooms after five epochs of training. There appears to be evidence of visual under-fitting via repeated textures across multiple samples.

Bedrooms after 1 epoch

Generated bedrooms after one training pass through the dataset. Theoretically, the model could learn to memorize training examples, but this is experimentally unlikely as we train with a small learning rate and minibatch SGD. We are aware of no prior empirical evidence demonstrating memorization with SGD and a small learning rate in only one epoch.

Walking from one point to another in bedroom latent space

Interpolation between a series of 9 random points in Z show that the space learned has smooth transitions, with every image in the space plausibly looking like a bedroom. In the 6th row, you see a room without a window slowly transforming into a room with a giant window. In the 10th row, you see what appears to be a TV slowly being transformed into a window.

Forgetting to draw windows

Top row: un-modified samples from model. Bottom row: the same samples generated with dropping out ”window” filters. Some windows are removed, others are transformed into objects with similar visual appearance such as doors and mirrors. Although visual quality decreased, overall scene composition stayed similar, suggesting the generator has done a good job disentangling scene representation from object representation. Extended experiments could be done to remove other objects from the image and modify the objects the generator draws.

Google image search from generations

Arithmetic on faces

Rotations are linear in latent space

More faces

Album covers

Imagenet generations