I cannot tell if this error is due to a technical mistake or hyper-parameters, but my DC-GAN's discriminator loss starts low and gradually climbs higher, slowing down around 8, whereas my generator loss goes way down. I ended it at about 60,000 epochs. Funny enough, the discri...
The discriminator and generator loss curves are depicted in Fig. 8.4. From the patterns in the loss curves, it can be seen that both discriminator loss and generator loss decrease up to about 2500th iteration. After about this iteration point, generator loss is increasing rapidly and discriminato...
在BIGGAN的基础上实现了具有U-Net结构的Discriminator,并且借助CutMix技术和consistency regularization loss进行训练,强大的Discriminator迫使Generator提升能力,从而获得更高质量的图像。 Reference [1] Brock, Andrew, Jeff Donahue, and Karen Simonyan. "Large Scale GAN Training for High Fidelity Natural Image Synthesi...
generator_loss = self.gan.gan_model.train_on_batch(x_latent_space_samples,y_generated_labels)print('Batch: '+str(int(b))+', [Discriminator:: Loss: '+str(discriminator_loss)+'], [ Generator :: Loss: '+str(generator_loss)+']')ifb % self.CHECKPOINT ==0: label = str(e)+'_'+st...
Generator-DiscriminatorELECTRA最主要的贡献是提出了新的预训练任务和框架,把生成式的Maskedlanguagemodel(MLM)预训练任务改成了判别式的Replacedtoken...判别器时计算了所有token上的loss,而以往计算BERT的MLMloss时会忽略没被mask的token。 参考:ELECTRA: 超越BERT, 19年最佳NLP预训练模型...
🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥 generator tensorflow discriminator vggnet super-resolution srgan image-enhancement relativistic-discriminator ragan relativistic-gan esrgan content-loss efficientnet div2k style-loss perceptual-loss ...
Discriminator’s lossGeneratorEfficiencyDifferentially private generative adversary network (GAN) is a very promising field in data privacy, with many practical real-world applications. The idea of differentially private GAN is to provide differential privacy protection for sensitive training datasets. By ...
print("Discriminatorloss :{}".format(d_cost)) print("Generator loss :{}".format(g_cost)) print("---")ifcurrent_step % self.FLAGS.checkpoint_every ==0: path = self.saver.save(self.sess, self.checkpoint_prefix, global_step=current_step)print("\nSaved model checkpoint to {}\n".form...
is the only loss that is computed for this batch. The probability increases from 0 to 0.5 over the course of the specified warmup epochs. If False, the CutMix augmentation and consistency loss are computed for every batch and added to the default GAN loss. In the case of a warm...
The generator is trained like this: g_optim.zero_grad() fake_pred = d(g(noise_batch)) g_loss = g_loss_fn(fake_pred, torch.ones(noise_batch_size, 1)) g_loss.backward() g_optim.step() It is mentioned thatd(g(noise_batch).detach())is written for the discriminator instead ofd(...