VQ-VAE - 矢量量化自动编码器.pdf,Neural Discrete Representation Learning Aaron van den Oord Oriol Vinyals Koray Kavukcuoglu DeepMind DeepMind DeepMind avdnoord@ vinyals@ korayk@ 8 1 0 Abstract 2 y Learning useful representations without supervision remain
commitment loss也比较简单,直接计算encoder的输出和对应的量化得到的embedding向量的L2误差: 注意这里的sg是作用在embedding向量上,这意味着这个约束只会影响encoder。 综上,VQ-VAE共包含三个部分的训练loss:reconstruction loss,VQ loss,commitment loss。 其中reconstruction loss作用在encoder和decoder上,VQ loss用来...
self._decoder = Decoder(embedding_dim, num_hiddens, num_residual_layers, num_residual_hiddens) def forward(self, x): z = self._encoder(x) z = self._pre_vq_conv(z) loss, quantized, perplexity, _ = self._vq_vae(z) x_recon = self._decoder(quantized) return loss, x_recon, perplex...
I want to choose photo before execute navigation.navigate(), but async/await doesn't work. I tried to change getphotoFromCamera function in Get_Image.js to async function and added await code to launc... Not able to download the excel while using response.flush for each row ...
GANs optimize a minimax objective with a generator neural network producing images by mapping random noise onto an image, and a discriminator defining the generators’ loss function by classifying its samples as real or fake. Larger scale GAN models can now generate high-quality and high- ...
randomnoiseontoanimage,andadiscriminatordefiningthegenerators’lossfunctionbyclassifying itssamplesasrealorfake.LargerscaleGANmodelscannowgeneratehigh-qualityandhigh- resolutionimages[4,13].However,itiswellknownthatsamplesfromthesemodelsdonotfully capturethediversityofthetruedistribution.Furthermore,GANsarechallenging...
Apply reconstruction loss on the time domain only, while still modeling a discrete latent space derived from the time-frequency domain. [2024.07.23] Use the Snake activation function [6] in the encoder and decoder, replacing (Leaky)ReLU. ...
So far, I tried naively modifying the train function in train_vqvae.py like so: # ... for i, (img, label) in enumerate(loader): model.zero_grad() img = img.to(device) with torch.cuda.amp.autocast(): out, latent_loss = model(img) recon_loss = criterion(out, img) latent_los...
VQ-VAE最终的loss function为: 第一项就是reconstruction loss。由于我们使用了ST estimator,embedding table无法得到梯度,因此才有了第二项。作者使用最简单的vector quantisation (VQ) 算法,即最小化z_{e}(x)和embeddinge之间的l2 距离,这会使得e接近encoder的输出。注意sg表示stopgradient operator,即在前向计算...
encoder(x)x_recon=self.decoder(z)returnx_recon,mean,log_vardefloss_function(x,x_recon,mean,log_var):recon_loss=nn.BCELoss(reduction='sum')(x_recon,x)kl_div=-0.5*torch.sum(1+log_var-mean.pow(2)-log_var.exp())returnrecon_loss+kl_div# 示例用法input_dim=784hidden_dim=400latent_di...