第一阶段是Image Quantization,借助ViT,将256x256的图片编码成32x32的离散latent codes,codebook size是8192,这里为了提升训练效果,用到了logit-laplace loss, l2 loss, adversarial loss and perceptual loss等loss。第二阶段是Vector-quantized Image Modeling,用第一阶段的模型得到的32x32共1024个tokens,让Transformer ...
一、用VIM提升image generation和image understanding任务的关键点在于一个好的image quantizer 二、发现在stage2用更大的计算量并且保持stage1中transformer的轻量级是有益的 Method 一、Vector-Quantized Images with ViT-VQGAN 二、Vector-Quantized Image Modeling Experiment 一、重建 二、生成 三、无监督学习 论文地...
Current image-to-image translation methods formulate the task with conditional generation models, leading to learning only the recolorization or regional changes as being constrained by the rich structural information provided by the conditional contexts. In this work, we propose introducing the vector ...
Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that involves pretraining a Transformer to predict rasterized image tokens autoregressively. The discrete image tokens are encoded from a learned Vision-Transformer-based VQGAN (ViT-VQGAN). We first propose ...
A PyTorch implementation ofContinuous Relaxation Training of Discrete Latent Variable Image Models. Ensure you have Python 3.7 and PyTorch 1.2 or greater. To train theVQVAEmodel with 8 categorical dimensions and 128 codes per dimension run the following command: ...
Usage: python demo.py -i inputs/whole_imgs -o results -v 2.0 -s 2 -f 0.1 [options]...-h show this help-i input Input image or folder. Default: inputs/whole_imgs-o output Output folder. Default: results-v version VQFR model version. Option: 1.0. Default: 1.0-f fidelity_ratio...
Neuroimage 2015, 114, 438–447. [Google Scholar] [CrossRef] [PubMed] Ahn, M.; Ahn, S.; Hong, J.H.; Cho, H.; Kim, K.; Kim, B.S.; Chang, J.W.; Jun, S.C. Gamma band activity associated with BCI performance: Simultaneous MEG/EEG study. Front. Hum. Neurosci. 2013, 7, ...
Recent advancement in deep generative models, which can represent high-dimension data in a low-dimension latent space efficiently when trained with big data, has been used to further reduce the sample size for image data compressive sampling. However, compressive sampling for 1D time series data ...
A fabric texture model is built on the gray-level histogram of textural fabric image. Two Gray-level Co-occurrence Matrix (GLCM) features are used to characterize the fabric texture. And an adaptive quantization scheme base on the texture mode is proposed to reduce the size of GLCM and ...
In “Vector-Quantized Image Modeling with Improved VQGAN”, we propose a two-stage model that reconceives traditional image quantization techniques to yield improved performance on image generation and image understanding tasks. In the first stage, an image quantization model, calledVQGAN, encodes an...