设输入图像x\in\mathbb R^{3\times H\times W},其编码器输出为z_e(x)\in\mathbb R^{c\times h\times w},量化操作的索引矩阵记作\text{index}(x)\in\mathbb N^{h\times w}. 一个误解是 VQ-VAE 的隐空间是所有\text{index}(x)构成的\mathbb N^{h\times w},但这是错误的!在第一小节中我...
1. VQVAE《Neural discrete representation learning》NeurIPS 2017 2. VQGAN 《Taming Transformers for High-Resolution Image Synthesis》CVPR 2021 3. ViT-VQGAN 《Vector-quantized Image Modeling with Impr…
To train theGS-Softmodel use--model=GSSOFT. Pretrained weights for theVQVAEandGS-Softmodels can be foundhere. TheVQVAEmodel gets ~4.82 bpd while theGS-softmodel gets ~4.6 bpd. Analysis of the Codebooks As demonstrated in the paper, the codebook matrices are low-dimensional, spanning only...
主要做法就是用VQ-VAE(参看Neural Discrete Representation Learning)自编码器来捕获一个动作可能造成的不同状态转移,然后和之前一样planning。具体来说,之前的muzero(参看Mastering atari, go, chess and shogi by planning with a learned model)的MCTS先是用一个representation function把状态转到压缩空间hh,然后用ff...
第三,VQ-GAN不再使用单一的均方差重构损失来比较输入图像像素和VAE解码器输出的像素,而是使用感知损失项,在编码器和对应的解码器的中间层计算特征图之间的差异,这种损失函数会产生更逼真的图像生成结果。 最后,VQ-GAN使用Transformer(而不是PixelCNN)作为模型的自回归部分,用于生成编码序列。Transformer在VQ-GAN完全训练...
The Vector-Quantized Variational AutoEncoder (VQ-VAE) is the foundation of the proposed method. The VQ-VAE model is trained to learn the non-linear mapping of degraded panchromatic image patches to high-resolution patches. This approach ensures that high-resolution patches can be recovered from ...
BigGAN-deep1.06.84203.6 IDDPM1.012.3N/A ADM-G, 1.0 guid.1.04.59186.7 VQVAE-21.0~31~45 VQGAN1.017.0470.6 VQGAN0.510.26125.5 VQGAN0.257.35188.6 ViT-VQGAN (Ours)1.04.17175.1 ViT-VQGAN (Ours)0.53.04227.4 Fréchet Inception Distance (FID) comparison between different models for class-conditional...
CVGAN: Image Generation with Capsule Vector-VAE In unsupervised learning, the extraction of a representational learning space is an open challenge in machine learning. Important contributions in this field are: the Variational Auto-Encoder ( VAE ), on a continuous latent representatio... R Pucci,...
Here, we propose a Vector Quantized Variational Autoencoder (VQ-VAE) neural F0 model that is both more efficient and more interpretable than the DAR. This model has two stages: one uses the VQ-VAE framework to learn a latent code for the F0 contour of each linguistic unit, and other ...
Vanilla VQVAEs usually suffer from low codebook usage due to the poor initialization of the codebook. Therefore, during training a significant portion of codes are rarely used, or dead. The reduction in effective codebook size results in worse reconstructions in stage 1 quantizer training and poor...