Key wordsGenerative adversarial network; Unsupervised learning; The objective function 1 GAN简介 生成式对抗网络(GAN, Generative Adversarial Networks )是一种深度学习模型,是近年来复杂分布上无监督学习最具前景的方法之一。GAN这一概念是由Ian Goodfellow于2014年提出,并迅速成为了非常火热的研究话题。目前,GAN的变...
This paper addresses the problem of path prediction for multiple interacting agents in a scene, which is a crucial step for many autonomous platforms such as self-driving cars and social robots. We present SoPhie; an interpretable framework based on Generative Adversarial Network (GAN), which lever...
It is important to note that CNNs and GANs only tend to be combined in one way, said Matthew Mead, CTO at IT consultancy SPR. "GANs typically work with image data and can use CNNs as the discriminator. But this doesn't work the other way around, meaning a CNN cannot use a GAN,"...
zi2zi(字到字, meaning from character to character) is an application and extension of the recent popular pix2pix model to Chinese characters.Details could be found in this blog post.Network StructureOriginal ModelThe network structure is based off pix2pix with the addition of category embedding...
This can be fatal in applications where the underlying structure (e.g.., neurons, vessels, membranes, and road networks) of the image carries crucial semantic meaning. In this paper, we propose a novel GAN model that learns the topology of real images, i.e., connectedness and loopy-ness....
a discriminator network is trained to do the assessment, and since networks are differentiable, we also get a gradient we can use to steer both networks to the right direction. Typically, the generator is of main interest – the discriminator is an adaptive loss function that gets discarded onc...
Our model, dubbed, HFL-GAN (Hierarchical Federated Learning - Generative Adversarial Network) introduces two distinct hierarchical steps as follows. (1) Multi-generator GAN model per client: This ensures more robust training, and by ensuring each generator is trained differently, allowing the model(...
The traditional GAN network aims to generate synthetic image samples and minimize the loss value of the generator network. The discriminator is not providing enough information to generate the fake samples. The tabular GAN is used to create numerical samples instead of image samples. To calculate ...
HiFiGAN [4], a generative adversarial network (GAN) model that generates audio from mel spectrograms produced by the Multi-speaker FastPitch in (1). The generator uses transposed convolutions to upsample mel spectrograms to audio.Dataset
For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN...