mlsanityData to 7 Oct 2026

Megapixel Size Image Creation using Generative Adversarial Networks

arXiv 1706.00082 ↗First posted 31 May 2017cs.CV cs.GR cs.LGcited by 27

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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.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 May 2017.

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Citing papers per year: 2021: 7; 2022: 10; 2023: 6; 2024: 4.

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  1. 2021Efficient Geometry-aware 3D Generative Adversarial Networkscited by 1,204
  2. 2024Guiding a Diffusion Model with a Bad Version of Itselfcited by 286
  3. 2023StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesiscited by 212
  4. 2021Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editingcited by 91
  5. 2021LOLNeRF: Learn from One Lookcited by 84
  6. 2022Efficient Video Prediction via Sparsely Conditioned Flow Matchingcited by 54
  7. 2022Self-Distilled StyleGAN: Towards Generation from Internet Photoscited by 37
  8. 2022State-of-the-Art in the Architecture, Methods and Applications of StyleGANcited by 34
  9. 2022Studying Bias in GANs through the Lens of Racecited by 31
  10. 2021Generative Adversarial Graph Convolutional Networks for Human Action Synthesiscited by 23
  11. 2022The Euclidean Space is Evil: Hyperbolic Attribute Editing for Few-shot Image Generationcited by 22
  12. 2022Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Valuescited by 21
15 more
  1. 2024An Interpretable Evaluation of Entropy-based Novelty of Generative Modelscited by 17
  2. 2021Hallucinating Pose-Compatible Scenescited by 14
  3. 2023LumiGAN: Unconditional Generation of Relightable 3D Human Facescited by 14
  4. 2024Be More Diverse than the Most Diverse: Optimal Mixtures of Generative Models via Mixture-UCB Bandit Algorithmscited by 13
  5. 2022Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanicscited by 11
  6. 2022Hierarchical Semantic Regularization of Latent Spaces in StyleGANscited by 8
  7. 2023Instance-Conditioned GAN Data Augmentation for Representation Learningcited by 7
  8. 2022Multi-level Latent Space Structuring for Generative Controlcited by 4
  9. 2022Exploring and Exploiting Hubness Priors for High-Quality GAN Latent Samplingcited by 1
  10. 2023F?D: On understanding the role of deep feature spaces on face generation evaluationcited by 1
  11. 2023Feature Proliferation -- the "Cancer" in StyleGAN and its Treatmentscited by 1
  12. 2021Truncated Log-concave Sampling with Reflective Hamiltonian Monte Carlocited by 0
  13. 2021Ebola Optimization Search Algorithm (EOSA): A new metaheuristic algorithm based on the propagation model of Ebola virus diseasecited by 0
  14. 2023DPGOMI: Differentially Private Data Publishing with Gaussian Optimized Model Inversioncited by 0
  15. 2024Content-Aware Preserving Image Generationcited by 0