The LSGAN can be implemented with a minor change to the output layer of the discriminator layer and the adoption of the least squares, or L2, loss function. In this tutorial, you will discover how to develop a least squares generative adversarial network.

GAN Least Squares Loss. GAN Least Squares Loss is a least squares loss function for generative adversarial networks. Minimizing this objective function is equivalent to minimizing the Pearson $\chi^ {2}$ divergence. The objective function (here for LSGAN) can be defined as: $$ \min_ {D}V_ {LS}\left (D\right) = \frac {1} {2}\mathbb {E}_ {\mathbf {x} \sim p_ {data}\left (\mathbf {x}\right)}\left [\left (D\left (\mathbf {x}\right) - b\right)^ {2}\right] + \frac {1} {2}\mathbb {E}_ {\mathbf {z

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In this tutorial, you will discover how to develop a least squares generative adversarial network. After completing this tutorial, you will know: 2020-12-11 Loss-Sensitive Generative Adversarial Networks (LS-GAN) in torch, IJCV - maple-research-lab/lsgan 2018-08-23 2017-01-10 2017-05-01 To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator. We show that minimizing the objective function of LSGAN yields minimizing the Pearson X2 divergence. There are two benefits of LSGANs over regular GANs. I am wondering that if the generator will oscillating during training using wgan loss or wgan-gp loss instead of lsgan loss because the wgan loss might be negative value. I replaced the lsgan loss with wgan/wgan-gp loss (the rest of parameters and model structures were same) for horse2zebra transfer mission and I found that the model using wgan/wgan-gp loss can not be trained: 2017-07-19 The LSGAN is a modification to the GAN architecture that changes the loss function for the discriminator from binary cross entropy to a least squares loss.

学習過程の実装. まず、LAGANの目的関数は以下のようになります。. Copied! D_loss = 0.5 * (torch.sum( (D_true - b) ** 2) + torch.sum( (D_fake - a) ** 2)) / batchsize G_loss = 0.5 * (torch.sum( (D_fake - c) ** 2)) / batchsize. ただし. Copied! a, b, c = 0, 1, 1. これを誤差関数として、パラメータの更新を行います。.

The motivation for this change is that the least squares loss will penalize generated images based on their distance from the decision boundary. Two popular alternate loss functions used in many GAN implementations are the least squares loss and the Wasserstein loss. Despite a very rich research activity leading to numerous interesting GAN algorithms, it is still very hard to assess which algorithm(s) perform better than others. Chapter 15: How to Develop a Least Squares GAN (LSGAN) CycleGAN loss function.

I am wondering that if the generator will oscillating during training using wgan loss or wgan-gp loss instead of lsgan loss because the wgan loss might be negative value. I replaced the lsgan loss with wgan/wgan-gp loss (the rest of parameters and model structures were same) for horse2zebra transfer mission and I found that the model using wgan/wgan-gp loss can not be trained:

In regular GAN, the discriminator uses cross-entropy loss function which sometimes leads to vanishing gradient problems. Instead of that lsGAN proposes to use the least-squares loss function for the discriminator. WGAN-GP and LSGAN versions of my GAN both completely fail to produce passable images even after 25 epochs. I use nn.MSELoss() for the LSGAN version of my GAN. I don’t use any tricks like one-sided label smoothing, and I train with default learning rats in both the LSGAN and WGANGP papers. Trong series GAN này mình đã giới thiệu về ý tưởng của mạng GAN, cấu trúc mạng GAN với thành phần là Generator và Discriminator, GAN loss function. Tuy nhiên GAN loss function không tốt, nó bị vanishing gradient khi train generator bài này sẽ tìm hiểu hàm LSGAN để giải quyết vấn đề trên. gamma: this is the coefficient for loss-minimization term (the first term in the objective for optimizing L_\theta).

Và không bị hiện tượng vanishing gradient như hàm sigmoid do đó có thể train được Generator tốt hơn. Keras-GAN / lsgan / lsgan.py / Jump to Code definitions LSGAN Class __init__ Function build_generator Function build_discriminator Function train Function sample_images Function LSGAN.html. 2 Related Work Deep generative models, especially the Generative Adversarial Net (GAN) [13], have attracted many attentions recently due to their demonstrated abilities of generating real samples following Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities this loss function may lead to the vanishing gradients prob-lem during the learning process.
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参考：知乎专栏：条条大路通罗马LS-GAN：把GAN建立在Lipschitz密度上 代码：https://github This might be a peculiarity with the Wasserstein loss. I assume that using different learning rates and architectures would help.

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