# TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation

arXiv: https://arxiv.org/abs/1901.09953 · HTML: https://mlsanity.com/paper/1901.09953/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2019.

## Built on by (most cited first, 7 of 7)

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- [Generative Adversarial Networks for Image Super-Resolution: A Survey](https://mlsanity.com/paper/2204.13620/) (2022)
- [INR-V: A Continuous Representation Space for Video-based Generative Tasks](https://mlsanity.com/paper/2210.16579/) (2022)
- [PhyEduVideo: A Benchmark for Evaluating Text-to-Video Models for Physics Education](https://mlsanity.com/paper/2601.00943/) (2026)
- [Deep Generative Modeling-based Data Augmentation with Demonstration using the BFBT Benchmark Void Fraction Datasets](https://mlsanity.com/paper/2308.10120/) (2023)
- [MetaScript: Few-Shot Handwritten Chinese Content Generation via Generative Adversarial Networks](https://mlsanity.com/paper/2312.16251/) (2023)

## Abstract (arXiv)

Deep generative models have been successfully applied to many applications. However, existing works experience limitations when generating large images (the literature usually generates small images, e.g. 32 * 32 or 128 * 128). In this paper, we propose a novel scheme, called deep tensor adversarial generative nets (TGAN), that generates large high-quality images by exploring tensor structures. Essentially, the adversarial process of TGAN takes place in a tensor space. First, we impose tensor structures for concise image representation, which is superior in capturing the pixel proximity information and the spatial patterns of elementary objects in images, over the vectorization preprocess in existing works. Secondly, we propose TGAN that integrates deep convolutional generative adversarial networks and tensor super-resolution in a cascading manner, to generate high-quality images from random distributions. More specifically, we design a tensor super-resolution process that consists of tensor dictionary learning and tensor coefficients learning. Finally, on three datasets, the proposed TGAN generates images with more realistic textures, compared with state-of-the-art adversarial autoencoders. The size of the generated images is increased by over 8.5 times, namely 374 * 374 in PASCAL2.