To address this problem, a conditional patch-based generative adversarial network is proposed which utilizes a generator network and a patch-based discriminator network conditioned on the sample data with an additional loss function to learn both thin and thick vessels. Experiments are conducted on ...
而每一层都有一个相同size的Discriminator,它会在相同尺度的patch上做判别,但因为图片的分辨率是逐层增加的,所以D的感受野其实是在逐层减小的,所以整体是一个coarse-to-fine的生成框架。 GPNN框架 这篇工作的结构和sinGAN很相似,只是把Generator从GAN换成了PNN(Patch Nearest Neighbor),每层分辨率上升的比例、总的...
Convergence is achieved when the discriminators are unable to distinguish between synthetic and real input CTs. For visual clarity, the operational blocks in the (b) generator and (c) discriminator architectures are presented as non-cubic blocks. Full size image Loss functions The adversarial loss ...
Two methods including forgery scores computation using frequency analysis and trained discriminator models are proposed to identify counterfeiting. To visualize the depth of the brushstrokes datasets, we use the Reflectance Transformation Imaging technique. We evaluate the authentication of oil paintings by ...
When the integration time is not large enough, the discriminators could suffer, due to residual errors caused by high-frequency components of ir(n) and qr(n) which eventually lead to a reduction of the tracking accuracy. After the integration process, Ir(n) and Qr(n) can be represented ...
Second, patch discriminator was utilized to convert the dichotomy of the sample into that multiple partial images of the same size to ensure that the generated results can retain more detailed features. Finally, the loss function of FDPPGAN comprised perceptual feature loss, content loss, generator...
Second, patch discriminator was utilized to convert the dichotomy of the sample into that multiple partial images of the same size to ensure that the generated results can retain more detailed features. Finally, the loss function of FDPPGAN comprised perceptual feature los...
The model uses the classical network U-net as the generator, and it uses the dual adversarial mechanism based on the Markovian discriminator (PatchGAN) to enhance the generator's attention to high-frequency information. To ensure that the network satisfies the Lipschitz cont...
A patch GAN-based depth completion device is provided that stores program instructions executable by the processor to generate a dense final depth map, divide the final depth map and depth measurement data into a plurality of patches in a discriminator of the generative adversarial neural network, ...
The discriminator uses the classical convolutional neural network and adds dense connection blocks to the discriminator network, which improves the discriminator's recognition ability and can train the adversarial model better. The proposed method has been tested on two public databases, STARE and DRIVE...