unsupervised learning:超参跟无条件图片合成一样,用ViT-VQGAN-SS,在Transformer某一个块上用平均池化取得特征,实验表明,中间层(15/36 for large, 10/24 for base)的特征有更好的分类精度 表7 ImageNet上不同无监督学习方法的线性探测精度,DALLE dVAE的图片quantizer用了额外的数据训练而成,而VIM-Large未使用dr...
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 ...
@inproceedings{anonymous2022vectorquantized, title = {Vector-quantized Image Modeling with Improved {VQGAN}}, author = {Anonymous}, booktitle = {Submitted to The Tenth International Conference on Learning Representations }, year = {2022}, url = {https://openreview.net/forum?id=pfNyExj7z2}, ...
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...
Quantized gradients need to be carried out with care since unsuitable implementations run the risk of causing the gradient descent algorithm not to converge. Show moreView article Chapter Lattice Vector Quantization for Wavelet-Based Image Coding Advances in Imaging and Electron Physics Book series1999,...
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…
传统观点认为,用于图像生成的自回归模型 (Autoregressive model) 通常使用矢量量化 (Vector-quantized) 的token。MAR 观察到这个观点站不住脚,即:离散值空间分布对于自回归建模不是必需的。 在MAR 这项工作中,作者提出使用扩散过程对每个token 的概率分布进行建模,这使 MAR 能够在连续值空间中应用自回归模型。 MAR ...