2017年12月28日 · Autoregressive generative models consistently achieve the best results in density estimation tasks involving high dimensional data, such as images or audio. They pose …

PixelSNAIL的效果是远比PixelCNN的效果好的，而且，这里并不知道如何实现自注意力机制，所以需要好学习一下他的代码。 很烦，之前弄得深度学习环境因为系统快照的问题，需要重新安 …

2023年9月2日 · 本文介绍了PixelSNAIL，一种将因果卷积和自注意力机制融合的自回归模型，用于解决高维度数据生成中的长依赖问题。 实验结果显示在CIFAR-10和ImageNet上优于其他单一 …

Autoregressive generative models consistently achieve the best results in density estimation tasks involving high dimensional data, such as images or audio.

2017年12月28日 · Pixel-SNAIL is comprised of residual blocks that carry out causal convolutions and attention blocks that produce keys and values from input data.

PixelSNAIL extends PixelCNN [2] (and its variants) by introducing a causally masked attention layer. This layer extends the model's receptive field by allowing each pixel to explicitly depend …

2018年10月11日 · The idea of PixelSNAIL is to combine a residual block and a self-attention block. Receptive field for a randomly initialized model (Derivative of the predicted yellow pixel …

In this paper, we show how 1) neural attention and 2) meta learning techniques can be used in combination with autoregressive models to enable effective few-shot density estimation. Our …

Autoregressive generative models consistently achieve the best results in density estimation tasks involving high dimensional data, such as images or audio. They pose density estimation as a …

PyTorch implementation of PixelSnail, an auto-regressive, generative model. title={PixelSNAIL: An Improved Autoregressive Generative Model}, . author={Xi Chen and Nikhil Mishra and …