Downsampled Imagenet
No Label
Common
|...
许可协议: Unknown

Overview

This page includes downsampled ImageNet images, which can be used for density estimation and generative modeling experiments. Images come in two resolutions: 32x32 and 64x64, and were introduced in Pixel Recurrent Neural Networks. Please refer to the Pixel RNN paper for more details and results.

数据概要
数据格式
Image,
数据量
--
文件大小
15.71GB
发布方
Stanford Vision Lab
The Stanford Vision and Learning Lab (SVL) at Stanford is directed by Professors Fei-Fei Li, Juan Carlos Niebles, Silvio Savarese and Jiajun Wu. We are tackling fundamental open problems in computer vision research and are intrigued by visual functionalities that give rise to semantically meaningful interpretations of the visual world.
数据集反馈
| 342 | 数据量 -- | 大小 15.71GB
Downsampled Imagenet
No Label
Common
许可协议: Unknown

Overview

This page includes downsampled ImageNet images, which can be used for density estimation and generative modeling experiments. Images come in two resolutions: 32x32 and 64x64, and were introduced in Pixel Recurrent Neural Networks. Please refer to the Pixel RNN paper for more details and results.

数据集反馈
0
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