On the Effects of Batch and Weight Normalization in Generative Adversarial Networks On the Effects of Batch and Weight Normalization in Generative Adversarial Networks
Paper summary * They analyze the effects of using Batch Normalization (BN) and Weight Normalization (WN) in GANs (classical algorithm, like DCGAN). * They introduce a new measure to rate the quality of the generated images over time. ### How * They use BN as it is usually defined. * They use WN with the following formulas: * Strict weight-normalized layer: * ![Strict WN layer](https://raw.githubusercontent.com/aleju/papers/master/neural-nets/images/On_the_Effects_of_BN_and_WN_in_GANs__strict_wn.jpg?raw=true "Strict WN layer") * Affine weight-normalized layer: * ![Affine WN layer](https://raw.githubusercontent.com/aleju/papers/master/neural-nets/images/On_the_Effects_of_BN_and_WN_in_GANs__affine_wn.jpg?raw=true "Affine WN layer") * As activation units they use Translated ReLUs (aka "threshold functions"): * ![TReLU](https://raw.githubusercontent.com/aleju/papers/master/neural-nets/images/On_the_Effects_of_BN_and_WN_in_GANs__trelu.jpg?raw=true "TReLU") * `alpha` is a learned parameter. * TReLUs play better with their WN layers than normal ReLUs. * Reconstruction measure * To evaluate the quality of the generated images during training, they introduce a new measure. * The measure is based on a L2-Norm (MSE) between (1) a real image and (2) an image created by the generator that is as similar as possible to the real image. * They generate (2) by starting `G(z)` with a noise vector `z` that is filled with zeros. The desired output is the real image. They compute a MSE between the generated and real image and backpropagate the result. Then they use the generated gradient to update `z`, while leaving the parameters of `G` unaltered. They repeat this for a defined number of steps. * Note that the above described method is fairly time-consuming, so they don't do it often. * Networks * Their networks are fairly standard. * Generator: Starts at 1024 filters, goes down to 64 (then 3 for the output). Upsampling via fractionally strided convs. * Discriminator: Starts at 64 filters, goes to 1024 (then 1 for the output). Downsampling via strided convolutions. * They test three variations of these networks: * Vanilla: No normalization. PReLUs in both G and D. * BN: BN in G and D, but not in the last layers and not in the first layer of D. PReLUs in both G and D. * WN: Strict weight-normalized layers in G and D, except for the last layers, which are affine weight-normalized layers. TPReLUs (Translated PReLUs) in both G and D. * Other * They train with RMSProp and batch size 32. ### Results * Their WN formulation trains stable, provided the learning rate is set to 0.0002 or lower. * They argue, that their achieved stability is similar to the one in WGAN. * BN had significant swings in quality. * Vanilla collapsed sooner or later. * Both BN and Vanilla reached an optimal point shortly after the start of the training. After that, the quality of the generated images only worsened. * Plot of their quality measure: * ![Losses over time](https://raw.githubusercontent.com/aleju/papers/master/neural-nets/images/On_the_Effects_of_BN_and_WN_in_GANs__losses_over_time.jpg?raw=true "Losses over time") * Their quality measure is based on reconstruction of input images. The below image shows examples for that reconstruction (each person: original image, vanilla reconstruction, BN rec., WN rec.). * ![Reconstructions](https://raw.githubusercontent.com/aleju/papers/master/neural-nets/images/On_the_Effects_of_BN_and_WN_in_GANs__reconstructions.jpg?raw=true "Reconstructions") * Examples generated by their WN network: * ![WN Examples](https://raw.githubusercontent.com/aleju/papers/master/neural-nets/images/On_the_Effects_of_BN_and_WN_in_GANs__wn_examples.jpg?raw=true "WN Examples")
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On the Effects of Batch and Weight Normalization in Generative Adversarial Networks
Sitao Xiang and Hao Li
arXiv e-Print archive - 2017 via Local arXiv
Keywords: stat.ML, cs.CV, cs.LG

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