# Train Sparsely, Generate Densely: Memory-Efficient Unsupervised Training of High-Resolution Temporal GAN

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/train-sparsely-generate-densely-memory-efficient-unsupervised-training-of-high/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Masaki Saito,Shunta Saito,Masanori Koyama,Sosuke Kobayashi |
| Citations | 111 |
| DOI | 10.1007/s11263-020-01333-y |
| Fields | Computer Science |
| Open Access | true |
| OA Status | green |
| OA URL | https://arxiv.org/pdf/1811.09245 |
| OpenAlex ID | https://openalex.org/W3031246127 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Masaki Saito](https://scholariq.org/researchers/masaki-saito/)

## Paper primary topic

- [Generative Adversarial Networks and Image Synthesis](https://scholariq.org/topics/generative-adversarial-networks-and-image-synthesis/)

## Paper topics

- [Generative Adversarial Networks and Image Synthesis](https://scholariq.org/topics/generative-adversarial-networks-and-image-synthesis/)
- [Advanced Image Processing Techniques](https://scholariq.org/topics/advanced-image-processing-techniques/)
- [Advanced Vision and Imaging](https://scholariq.org/topics/advanced-vision-and-imaging/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.