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GAN Has Really Influenced A Lot Of Research

GAN Has Really Influenced A Lot Of Research


GAN has been used in many studies since it was proposed to Ian J. Goodfellow. It is said to be a lot, but do you know how many of them are actually coming out in a month?

In arXiv alone, we are submitting about 90 (March), 70 (April), 60 (May), 100 (June), and 90 (July). The average number of submissions is 82 in January, and since we are receiving about 2-3 submissions a day, you can see that the number is quite high. If you include other journals, you can expect to see 3 to 5 a day. The momentum is still going strong. Even more frightening is not the GAN, but the AI-related papers, which scares me just thinking about how many papers are coming out in a day.

So let's take a look at the GAN-related papers during the August period. GAN papers are coming out at this pace. It's hard to just keep up with them.


GIF: Generative Interpretable Faces

Shape Defense





Dual Attention GANs for Semantic Image Synthesis



Relational Data Synthesis using Generative Adversarial Networks: A Design Space Exploration

Adaptive WGAN with loss change rate balancing



Non-Parallel Voice Conversion with Augmented Classifier Star Generative Adversarial Networks

Crossing-Domain Generative Adversarial Networks for Unsupervised Multi-Domain Image-to-Image Translation

Direct Adversarial Training for GANs



Crossing-Domain Generative Adversarial Networks for Unsupervised Multi-Domain Image-to-Image Translation

MutaGAN  A Seq2seq GAN Framework to Predict Mutations of Evolving Protein Populations

Self-Supervised Human Activity Recognition by Augmenting Generative Adversarial Networks

Anime-to-Real Clothing  Cosplay Costume Generation via Image-to-Image Translation

CAN: A Causal Adversarial Network for Learning Observational and Interventional Distributions



GAN Slimming  All-in-One GAN Compression by A Unified Optimization Framework



CSCL  Critical Semantic-Consistent Learning for Unsupervised Domain Adaptation

Fidelity-Controllable Extreme Image Compression with Generative Adversarial Networks

Deep Learning Based on Generative Adversarial and Convolutional Neural Networks for Financial Time Series Predictions



Ptolemy  Architecture Support for Robust Deep Learning



Symbolic Semantic Segmentation and Interpretation of COVID-19 Lung Infections in Chest CT volumes based on Emergent Languages



TAnoGAN  Time Series Anomaly Detection with Generative Adversarial Networks

CDE-GAN  Cooperative Dual Evolution Based Generative Adversarial Network

DTDN  Dual-task De-raining Network



Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning



Improving Text to Image Generation using Mode-seeking Function

Regularization And Normalization For Generative Adversarial Networks  A Review

Blur-Attention  A boosting mechanism for non-uniform blurred image restoration



Generative Adversarial Networks for Spatio-temporal Data  A Survey

CinC-GAN for Effective F0 prediction for Whisper-to-Normal Speech Conversion

Tdcgan  Temporal Dilated Convolutional Generative Adversarial Network for End-to-end Speech Enhancement



Robust Autoencoder GAN for Cryo-EM Image Denoising

Neutral Face Game Character Auto-Creation via PokerFace-GAN

Semi-Supervised Learning with GANs for Device-Free Fingerprinting Indoor Localization





Evaluating Lossy Compression Rates of Deep Generative Models





Synthesizing Property & Casualty Ratemaking Datasets using Generative Adversarial Networks

DF-GAN  Deep Fusion Generative Adversarial Networks for Text-to-Image Synthesis

Recurrent Deconvolutional Generative Adversarial Networks with Application to Text Guided Video Generation



Mitigating Dataset Imbalance via Joint Generation and Classification

Improving Stability of LS-GANs for Audio and Speech Signals

Improving the Performance of Fine-Grain Image Classifiers via Generative Data Augmentation

VAW-GAN for Singing Voice Conversion with Non-parallel Training Data



IGANI  Iterative Generative Adversarial Networks for Imputation Applied to Prediction of Traffic Data

R-MNet  A Perceptual Adversarial Network for Image Inpainting



GANDALF  Generative Adversarial Networks with Discriminator-Adaptive Loss Fine-tuning for Alzheimer's Disease Diagnosis from MRI

GANBERT  Generative Adversarial Networks with Bidirectional Encoder Representations from Transformers for MRI to PET synthesis

T-GD  Transferable GAN-generated Images Detection Framework



Intervention Generative Adversarial Networks



Non-Adversarial Imitation Learning and its Connections to Adversarial Methods



Fighting Deepfake by Exposing the Convolutional Traces on Images

Improving the Speed and Quality of GAN by Adversarial Training

Generative Adversarial Network for Radar Signal Generation

Oversampling Adversarial Network for Class-Imbalanced Fault Diagnosis



Generative Adversarial Network-Based Sinogram Super-Resolution for Computed Tomography Imaging

Generative Adversarial Networks for Image and Video Synthesis  Algorithms and Applications

HooliGAN  Robust High Quality Neural Vocoding

F2GAN  Fusing-and-Filling GAN for Few-shot Image Generation



GL-GAN  Adaptive Global and Local Bilevel Optimization model of Image Generation

Structure Preserving Stain Normalization of Histopathology Images Using Self-Supervised Semantic Guidance

Annealing Genetic GAN for Minority Oversampling

A feature-supervised generative adversarial network for environmental monitoring during hazy days



Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN

Multimodal Image-to-Image Translation via a Single Generative Adversarial Network

TOAD-GAN  Coherent Style Level Generation from a Single Example



Generative Adversarial Networks for Synthesizing InSAR Patches

A Spectral Energy Distance for Parallel Speech Synthesis

Analyzing the Components of Distributed Coevolutionary GAN Training

Learning Based Methods for Traffic Matrix Estimation from Link Measurements



Multi-level Wavelet-based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video

Point Cloud Completion by Learning Shape Priors



Joint Generative Learning and Super-Resolution For Real-World Camera-Screen Degradation

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