赶上最新的AI论文

GAN确实影响了很多研究。

论文

GAN自从向Ian J.Goodfellow提出后,已经被用于很多研究。说是很多,但你知道一个月到底有多少篇出来吗?

仅仅在arXiv上,我们提交的论文就有90篇(3月)、70篇(4月)、60篇(5月)、100篇(6月)、90篇(7月)。1月份的平均投稿量是82篇,由于我们每天都会收到2-3篇左右的投稿,可以看出这个数字是相当高的。如果算上其他期刊的话,一天可以看到3-5篇。势头依然强劲。更可怕的不是GAN,而是人工智能相关的论文,光是想想一天出多少论文,我就害怕了。

所以我们来看看8月期间GAN相关论文的情况。GAN论文以这样的速度出来。光是跟上他们的脚步就很难了。

8/31

GIF: Generative Interpretable Faces

Shape Defense

 

8/30

 

8/29

Dual Attention GANs for Semantic Image Synthesis

 

8/28

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

Adaptive WGAN with loss change rate balancing

 

8/27

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

 

8/26

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

 

8/25

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

 

8/24

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

 

8/23

Ptolemy  Architecture Support for Robust Deep Learning

 

8/22

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

 

8/21

TAnoGAN  Time Series Anomaly Detection with Generative Adversarial Networks

CDE-GAN  Cooperative Dual Evolution Based Generative Adversarial Network

DTDN  Dual-task De-raining Network

 

8/20

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

 

8/19

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

 

8/18

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

 

8/17

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

 

8/16

 

8/15

Evaluating Lossy Compression Rates of Deep Generative Models

 

8/14

 

8/13

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

 

8/12

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

 

8/11

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

R-MNet  A Perceptual Adversarial Network for Image Inpainting

 

8/10

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

 

8/9

Intervention Generative Adversarial Networks

 

8/8

Non-Adversarial Imitation Learning and its Connections to Adversarial Methods

 

8/7

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

 

8/6

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

 

8/5

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

 

8/4

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

 

8/3

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

 

8/2

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

Point Cloud Completion by Learning Shape Priors

 

8/1

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

  • メルマガ登録(ver
  • ライター
  • エンジニア_大募集!!
加藤 avatar
AI-SCHOLAR是一个评论媒体,以通俗易懂的方式介绍关于AI(人工智能)的最新文章。 人工智能的作用不仅限于技术创新,日本的科研能力正在下降,政府也在不断削减研究预算。 向世界传达人工智能的技术、应用以及支持人工智能的基础科学的背景,是一个重要的外延,可以极大地影响社会对科学的理解和印象。 AI-SCHOLAR旨在帮助消除普通民众和专家之间对人工智能的理解差距,为人工智能融入社会做出贡献。 另外,我们希望帮助大家把自己的学习和研究经验在媒体上体现出来,在社会上表达出来。 任何人都可以用艰深的词汇来解释高深难懂的事情,但AI-SCHOLAR追求的是"可读性"和"可理解性",充分利用词汇和设计来传递信息,以此为媒介。

如果您对文章内容有任何改进建议等,请通过 "联系我们 "表格与爱学网编辑部联系。
如果您能通过咨询表与我们联系,我们将非常感激。

联系我们