# Generative Adversarial Networks and Image Synthesis

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/generative-adversarial-networks-and-image-synthesis/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the application of Generative Adversarial Networks (GANs) in image processing, including image synthesis, style transfer, representation learning, and unsupervised learning. The papers cover various techniques such as image inpainting, texture synthesis, and conditional generative models using deep learning and neural networks. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t10775 |
| Works | 95 |

## Topic papers all

Showing 15 of 95.

- [Deep visual-semantic alignments for generating image descriptions](https://scholariq.org/papers/deep-visual-semantic-alignments-for-generating-image-descriptions/)
- [Image inpainting](https://scholariq.org/papers/image-inpainting/)
- [MesoNet: a Compact Facial Video Forgery Detection Network](https://scholariq.org/papers/mesonet-a-compact-facial-video-forgery-detection-network/)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://scholariq.org/papers/score-based-generative-modeling-through-stochastic-differential-equations/)
- [Capsule-forensics: Using Capsule Networks to Detect Forged Images and Videos](https://scholariq.org/papers/capsule-forensics-using-capsule-networks-to-detect-forged-images-and-videos/)
- [A Comprehensive Survey of Image Augmentation Techniques for Deep Learning](https://scholariq.org/papers/a-comprehensive-survey-of-image-augmentation-techniques-for-deep-learning/)
- [Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward](https://scholariq.org/papers/deepfakes-generation-and-detection-state-of-the-art-open-challenges/)
- [Deep learning for deepfakes creation and detection: A survey](https://scholariq.org/papers/deep-learning-for-deepfakes-creation-and-detection-a-survey/)
- [Generalized Autoencoder: A Neural Network Framework for Dimensionality Reduction](https://scholariq.org/papers/generalized-autoencoder-a-neural-network-framework-for-dimensionality-reduction/)
- [Temporal Generative Adversarial Nets with Singular Value Clipping](https://scholariq.org/papers/temporal-generative-adversarial-nets-with-singular-value-clipping-2/)
- [CogView: Mastering Text-to-Image Generation via Transformers](https://scholariq.org/papers/cogview-mastering-text-to-image-generation-via-transformers/)
- [GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks](https://scholariq.org/papers/gan-augmentation-augmenting-training-data-using-generative-adversarial-networks/)
- [Distinguishing computer graphics from natural images using convolution neural networks](https://scholariq.org/papers/distinguishing-computer-graphics-from-natural-images-using-convolution-neural/)
- [A new active labeling method for deep learning](https://scholariq.org/papers/a-new-active-labeling-method-for-deep-learning/)
- [Deepfake detection by human crowds, machines, and machine-informed crowds](https://scholariq.org/papers/deepfake-detection-by-human-crowds-machines-and-machine-informed-crowds/)

## Topic primary papers

Showing 15 of 34.

- [Image inpainting](https://scholariq.org/papers/image-inpainting/)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://scholariq.org/papers/score-based-generative-modeling-through-stochastic-differential-equations/)
- [Temporal Generative Adversarial Nets with Singular Value Clipping](https://scholariq.org/papers/temporal-generative-adversarial-nets-with-singular-value-clipping-2/)
- [CogView: Mastering Text-to-Image Generation via Transformers](https://scholariq.org/papers/cogview-mastering-text-to-image-generation-via-transformers/)
- [GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks](https://scholariq.org/papers/gan-augmentation-augmenting-training-data-using-generative-adversarial-networks/)
- [A new active labeling method for deep learning](https://scholariq.org/papers/a-new-active-labeling-method-for-deep-learning/)
- [Cross-Modality Image Synthesis from Unpaired Data Using CycleGAN](https://scholariq.org/papers/cross-modality-image-synthesis-from-unpaired-data-using-cyclegan/)
- [Recent Advances of Generative Adversarial Networks in Computer Vision](https://scholariq.org/papers/recent-advances-of-generative-adversarial-networks-in-computer-vision/)
- [paGAN](https://scholariq.org/papers/pagan/)
- [Learning to Decompose and Disentangle Representations for Video Prediction](https://scholariq.org/papers/learning-to-decompose-and-disentangle-representations-for-video-prediction/)
- [DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Models](https://scholariq.org/papers/diffumask-synthesizing-images-with-pixel-level-annotations-for-semantic/)
- [Train Sparsely, Generate Densely: Memory-Efficient Unsupervised Training of High-Resolution Temporal GAN](https://scholariq.org/papers/train-sparsely-generate-densely-memory-efficient-unsupervised-training-of-high/)
- [Deep clustering with a Dynamic Autoencoder: From reconstruction towards centroids construction](https://scholariq.org/papers/deep-clustering-with-a-dynamic-autoencoder-from-reconstruction-towards-centroids/)
- [Image Inpainting Based on Generative Adversarial Networks](https://scholariq.org/papers/image-inpainting-based-on-generative-adversarial-networks/)
- [GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations](https://scholariq.org/papers/genesis-generative-scene-inference-and-sampling-with-object-centric-latent/)

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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.