# Megapixel Size Image Creation using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1706.00082 · HTML: https://mlsanity.com/paper/1706.00082/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2017.

## Built on by (most cited first, 27 of 27)

- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [Guiding a Diffusion Model with a Bad Version of Itself](https://mlsanity.com/paper/2406.02507/) (2024)
- [StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis](https://mlsanity.com/paper/2301.09515/) (2023)
- [Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing](https://mlsanity.com/paper/2104.14754/) (2021)
- [LOLNeRF: Learn from One Look](https://mlsanity.com/paper/2111.09996/) (2021)
- [Efficient Video Prediction via Sparsely Conditioned Flow Matching](https://mlsanity.com/paper/2211.14575/) (2022)
- [Self-Distilled StyleGAN: Towards Generation from Internet Photos](https://mlsanity.com/paper/2202.12211/) (2022)
- [State-of-the-Art in the Architecture, Methods and Applications of StyleGAN](https://mlsanity.com/paper/2202.14020/) (2022)
- [Studying Bias in GANs through the Lens of Race](https://mlsanity.com/paper/2209.02836/) (2022)
- [Generative Adversarial Graph Convolutional Networks for Human Action Synthesis](https://mlsanity.com/paper/2110.11191/) (2021)
- [The Euclidean Space is Evil: Hyperbolic Attribute Editing for Few-shot Image Generation](https://mlsanity.com/paper/2211.12347/) (2022)
- [Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values](https://mlsanity.com/paper/2203.01993/) (2022)
- [An Interpretable Evaluation of Entropy-based Novelty of Generative Models](https://mlsanity.com/paper/2402.17287/) (2024)
- [Hallucinating Pose-Compatible Scenes](https://mlsanity.com/paper/2112.06909/) (2021)
- [LumiGAN: Unconditional Generation of Relightable 3D Human Faces](https://mlsanity.com/paper/2304.13153/) (2023)
- [Be More Diverse than the Most Diverse: Optimal Mixtures of Generative Models via Mixture-UCB Bandit Algorithms](https://mlsanity.com/paper/2412.17622/) (2024)
- [Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics](https://mlsanity.com/paper/2206.01693/) (2022)
- [Hierarchical Semantic Regularization of Latent Spaces in StyleGANs](https://mlsanity.com/paper/2208.03764/) (2022)
- [Instance-Conditioned GAN Data Augmentation for Representation Learning](https://mlsanity.com/paper/2303.09677/) (2023)
- [Multi-level Latent Space Structuring for Generative Control](https://mlsanity.com/paper/2202.05910/) (2022)
- [Exploring and Exploiting Hubness Priors for High-Quality GAN Latent Sampling](https://mlsanity.com/paper/2206.06014/) (2022)
- [F?D: On understanding the role of deep feature spaces on face generation evaluation](https://mlsanity.com/paper/2305.20048/) (2023)
- [Feature Proliferation -- the "Cancer" in StyleGAN and its Treatments](https://mlsanity.com/paper/2310.08921/) (2023)
- [Truncated Log-concave Sampling with Reflective Hamiltonian Monte Carlo](https://mlsanity.com/paper/2102.13068/) (2021)
- [Ebola Optimization Search Algorithm (EOSA): A new metaheuristic algorithm based on the propagation model of Ebola virus disease](https://mlsanity.com/paper/2106.01416/) (2021)
- [DPGOMI: Differentially Private Data Publishing with Gaussian Optimized Model Inversion](https://mlsanity.com/paper/2310.04528/) (2023)
- [Content-Aware Preserving Image Generation](https://mlsanity.com/paper/2411.09871/) (2024)

## Abstract (arXiv)

Since its appearance, Generative Adversarial Networks (GANs) have received a lot of interest in the AI community. In image generation several projects showed how GANs are able to generate photorealistic images but the results so far did not look adequate for the quality standard of visual media production industry. We present an optimized image generation process based on a Deep Convolutional Generative Adversarial Networks (DCGANs), in order to create photorealistic high-resolution images (up to 1024x1024 pixels). Furthermore, the system was fed with a limited dataset of images, less than two thousand images. All these results give more clue about future exploitation of GANs in Computer Graphics and Visual Effects.