New answers tagged

2

I took a look at your model. It seems you have incorrect architecture. The Conv2D layers in your D should have following params: (n_filters, kernel=3, padding='same'), where n_filters is the number of filters and it usually should be doubled as per DCGAN architecture. You can also use strides but since your images are small it won't make any sense. The D ...


1

in this tutorial, it taught us to intentionally provide false labels to "fool" the discriminator, does it make discriminator actually inaccurate? When training GANs, the training steps for the generator and discriminator are separate: There is a training stage for the discriminator, where it is presented with a mix of generated and real data, all ...


0

Your statement that we should be expecting some similarity in graphical patterns is not correct. The GAN loss takes the following form: $$ \min_G \max_D V(D,G) = \mathbb{E}_{x \sim p_{data}(x)} [\log D(x)] + \\ + \mathbb{E}_{z \sim p_z(z)} [\log (1 - D(G(z)))]\label{minimax} $$ We want to maximize this loss w.r.t. D in order to distinguish between real and ...


1

The authors of the paper Learning Robust Rewards with Adversarial Inverse Reinforcement Learning (2018, published in ICRL), which introduced the inverse RL technique AIRL, argue that GAIL fails to generalize to different environment's dynamics. Specifically, in section 7.2 (p. 7), they describe an experiment where they disable and shrink the two front legs ...


Top 50 recent answers are included