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SinGAN: Learning a Generative Model from a Single Natural Image: Difference between revisions
SinGAN: Learning a Generative Model from a Single Natural Image (view source)
Revision as of 21:51, 13 January 2020
, 13 January 2020→Generator
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Each generator consists of 5 convolutional blocks:<br> | Each generator consists of 5 convolutional blocks:<br> | ||
Conv(<math>3 \times 3</math>)-BatchNorm-LeakyReLU.<br> | Conv(<math>3 \times 3</math>)-BatchNorm-LeakyReLU.<br> | ||
They use 32 kernels per block at the coarsest scale and increase <math>2 \times</math> every 4 scales. | They use 32 kernels per block at the coarsest scale and increase <math>2 \times</math> every 4 scales.<br> | ||
===[https://arxiv.org/pdf/1502.03167.pdf Batch Normalization]=== | |||
; Definitions: | |||
* Internal Covariate Shift - the change in distribution of network activations as network parameters change. | |||
* Whitening | |||
===Discriminator=== | ===Discriminator=== | ||