2、DBCA(Deformable Bi-directional Cross-Attention) DBAC 结构并不复杂,如下图所示,本质是两个分支特征计算cross-attention,然后后面加了一个deformable conv,这里不过多介绍。 实验部分可以参考作者论文,这里也不过多介绍。
虽然论文的主要实验都是用 vq 项来更新 codebook 的,但我复现发现用 K-Means + EMA 的方式更新 codebook 效果更好。事实上,后续的 VQ-VAE-2 和很多其他工作都是用 EMA 方式更新 codebook 的。 Prior Learning VQ-VAE 训练结束后,我们就可以用它重构输入图像了。但是怎么直接生成新图像呢? 在说明这一点之前,...
VECTOR QUANTIZED AUTO-ENCODER CODEBOOK LEARNING FOR MANUFACTURING DISPLAY EXTREME MINOR DEFECTS DETECTIONA system including: a memory, an encoder, a decoder, and a processor, the processor being connected to the memory, the encoder, and the decoder. The system is configured to: receive, at the ...
We first propose multiple improvements over vanilla VQGAN from architecture to codebook learning, yielding better efficiency and reconstruction fidelity. The improved ViT-VQGAN further improves vector-quantized image modeling tasks, including unconditional, class-conditioned image generation and unsupervised ...
Codebook Learning Codebook这里主要关注训练过程中很多codes都没有被用到(rarely used or dead)的问题,导致codebook利用率低,显然,这会导致stage 1的quantizer训练效果不好,stage 2的image synthesis的diversity也不好。VQGAN采用了top-p和top-k采样来缓解这样的问题,而本文提出了两个改进,一个是用了一个新的codeboo...
2.Codebook Learning (1)问题:Vanilla VQVAE通常由于码本初始化不佳而导致码本使用率低。训练过程中,很大一部分code很少被使用,或者已经失效。导致stage1重建质量差,stage2中poor diversity。 (2)VQGAN 解决方式:topK和topP sampling heuristics,默认码本大小为1024,获得图像合成的最佳结果 (3)本文提出两种改进,使得...
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…
5.引用 [1]: Condenser: a Pre-training Architecture for Dense Retrieval [2]: PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers 发布于 2022-08-16 23:21 深度学习(Deep Learning) 计算机视觉 自监督学习 赞同274 条评论 分享喜欢收藏申请转载 ...
The VQ-VAE codebook contains 128 keys with 256 dimensions, and the latent space array size is set to 60×6060×60. During training, we utilize the Adam optimizer with a learning rate of 1×10−41×10−4 and incorporate dropout with a probability of 0.10.1. Training extends over 200 ...
The embedding space in a VQ-VAE model can be seen as a dictionary or codebook for the signals to be encoded. Once the model is trained, signals can be collected by drawing samples from the hidden space and passing them through the decoder part of the model. Here, we apply this model ...