后面几行都是跟第一行对比,可以看出基于StyleGAN的判别器就是比PatchGAN好不少,latent dim是指投影的低维空间的维度,16或8比较合适,同时第二个创新点的L2归一化对结果影响也很大 image synthesis:输入图片分辨率256x256,取batch size为1024,一共训练450,000个step。Adam优化器(参数有变),warm up,cosine schedule。
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...
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Transformera...
Systems and methods are provided for vector-quantized image modeling using vision transformers and improved codebook handling. In particular, the present disclosure provides a Vector-quantized Image Modeling (VIM) approach that involves pretraining a machine learning model (e.g., Transformer model) to...
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 ...
Vector Quantized Generative Adversarial Networks (VQGAN) is a generative model for image modeling. It was introduced in Taming Transformers for High-Resolution Image Synthesis. The concept is build upon two stages. The first stage learns in an autoencoder-like fashion by encoding images into a low...
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…
(2022 arxiv) BEIT V2- Masked Image Modeling with Vector-Quantized Visual Tokenizers 笔记 鹦鹉丛中笑 28 人赞同了该文章 目录 收起 1.论文动机 问题分析 解决方案 2.具体做法 2-1.码本的构建——VQ-KD 2-2. BEITv2部分 3.实验结果 3-1.分类任务 3-2.消融实验 4.结论 5.引用 作者列表 ...
"Vector quantized diffusion model for text-to-image synthesis." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10696-10706. 2022. ^Tang, Zhicong, Shuyang Gu, Jianmin Bao, Dong Chen, and Fang Wen. "Improved vector quantized diffusion models." arXiv...