最后一个是 discriminator。这个步骤有两部分,一种是只使用 autoencoder 生成的无雨滴图像,进行判断;另一种则是加入 attention map 作为指导。 判别器的损失函数是: 综上,整体损失为: 下面说一下实验部分。 生成器中残差块的实现: def _residual_block(self, input_tensor, name): """ attentive recurrent net...
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Method V (GAIL + Progressive Growing of Discriminator + Self-Imitation Learning from demonstrations + CLIB Expert Buffer): run the shell commandscripts/train_rl_agent/run_GAIL_use_all_door.sh SAC: run the shell commandscripts/train_rl_agent/run_SAC_door.sh ...
(4) It aims to improve the plausibility of the output coronary an- giograms with the coronary angiogram discriminator, thus fooling the discriminator that the generated GY (X) to be real. At the same time, the coronary angiogram discrimi- nator DY attempts to distinguish ...
discriminator(self.G, self.y, reuse=True) #self.sampler = self.sampler(self.z, self.y) # final summary operations self.d_sum = histogram_summary("d", self.D_logits) self.d__sum = histogram_summary("d_", self.D_logits_) self.G_sum = image_summary("G", self.G) # get loss ...
To be precise, we build a discriminator D that outputs an estimate of the probability D(z) that its input is a sample from q(z) rather than from \(\prod _{i=1}^k q(z_i)\). Then we have $$\begin{aligned} TC(q(z))= KL(q(z),\prod _{i=1}^k q(z_i)) = \mathbb ...
[31] merged an input clip with a generated frame to create a reformed video clip, which was then fed into the discriminator built by the 3D CNN(Convolutional Neural Network). The deep adversarial anomaly detection (DAAD) approach using task-specific characteristics was proposed by the authors ...
We implemented this by training the Bayesian texture-discriminator on natural image patches segmented by proximity and color, and the same decision boundary emerged as with explicit texture labels.Abhranil DasWilson GeislerJournal of Vision
Haghighi et al. [40] combined discriminative, restorative, and adversarial learning tasks to calculate losses and train the model, applying them to data types, such as CT, MRI, and X-ray. First, the model uses a separate discriminator to determine the differences between images through hyper...
This comes into a GAN where there is a generator and a discriminator that are always competing. The generator then produces fake data and on the other end, the discriminator measures and assess the real as well as the fake data that has been generated by the generator. This adversarial setup...