# TGANv2: Efficient Training of Large Models for Video Generation with Multiple Subsampling Layers

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/tganv2-efficient-training-of-large-models-for-video-generation-with-multiple/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Masaki Saito,Shunta Saito |
| Citations | 35 |
| Fields | Computer Science |
| Open Access | true |
| OA Status | green |
| OA URL | https://arxiv.org/pdf/1811.09245.pdf |
| OpenAlex ID | https://openalex.org/W2901599654 |
| Type | preprint |
| Year | 2018 |

## Paper authors

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

## Paper journal

- [arXiv (Cornell University)](https://scholariq.org/journals/arxiv-cornell-university/)

## Paper primary topic

- [Advanced Vision and Imaging](https://scholariq.org/topics/advanced-vision-and-imaging/)

## Paper topics

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

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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.