Vector-quantized Image Modeling (VIM)。训练transformer来自回归地预测32x32=1024个token,若是 class-conditioned 图片生成,跟vqgan一样把类别id的token放在图片token前面(输入模型)。加分类头是为了评估无监督学习的质量。 跟vqgan的差别: 阶段1的CNN换成ViT,因此解码器先将预测的每个token转换回8x8的图片patch,再...
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...
Vector-quantized Image Modeling 训好的ViT-VQGAN可以把图片encode然后得到一系列codebook的id,然后就可以用decoder-only的Transformer来autoregressively地学图像数据的分布,the density of image data P(x) ,如公式(3)所示。最终的目标是优化负对数似然(negative log-likelihood) L = \mathbb{E}_{x \in X}(-\...
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...
According to Shannon (1948), the amount of uncertainty R(x,y) of a value of x when we receive its quantized counterpart y from a transmission channel is given by (17)R(x,y)=h(x)−h(x|y) where h(x ) = ∫p(x) log p(x)dx is the differential entropy of a variable xThe ...
The optimal previsualized image vector quantization method for compressing digital images to a bit rate of 0.75 bpp or below with moderately low to very low subjective distortion is presented. The encoding method incorporates a visual model as part of the distortion measure. By modeling the quantiza...
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…