{"list":"other","total":56,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"1608.06993","title":"Densely Connected Convolutional Networks","created":"2016-08-25","cited_by_count":6720},{"id":"1812.04948","title":"A Style-Based Generator Architecture for Generative Adversarial Networks","created":"2018-12-12","cited_by_count":6013},{"id":"2203.00667","title":"Generative Adversarial Networks","created":"2022-03-01","cited_by_count":5627},{"id":"1611.07004","title":"Image-to-Image Translation with Conditional Adversarial Networks","created":"2016-11-21","cited_by_count":4527},{"id":"1409.4842","title":"Going Deeper with Convolutions","created":"2014-09-17","cited_by_count":4241},{"id":"1312.6114","title":"Auto-Encoding Variational Bayes","created":"2013-12-20","cited_by_count":4053},{"id":"1710.10196","title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","created":"2017-10-27","cited_by_count":2988},{"id":"1606.03498","title":"Improved Techniques for Training GANs","created":"2016-06-10","cited_by_count":2970},{"id":"1411.1784","title":"Conditional Generative Adversarial Nets","created":"2014-11-06","cited_by_count":2162},{"id":"1511.06434","title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","created":"2015-11-19","cited_by_count":2054},{"id":"1609.03499","title":"WaveNet: A Generative Model for Raw Audio","created":"2016-09-12","cited_by_count":1722},{"id":"1904.08779","title":"SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition","created":"2019-04-18","cited_by_count":1380},{"id":"2106.12423","title":"Alias-Free Generative Adversarial Networks","created":"2021-06-23","cited_by_count":1184},{"id":"1703.10593","title":"Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks","created":"2017-03-30","cited_by_count":707},{"id":"1701.04862","title":"Towards Principled Methods for Training Generative Adversarial Networks","created":"2017-01-17","cited_by_count":464},{"id":"2004.13408","title":"Time Series Forecasting With Deep Learning: A Survey","created":"2020-04-28","cited_by_count":459},{"id":"1805.06725","title":"GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training","created":"2018-05-17","cited_by_count":409},{"id":"2105.03075","title":"A Survey of Data Augmentation Approaches for NLP","created":"2021-05-07","cited_by_count":409},{"id":"1611.06455","title":"Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline","created":"2016-11-20","cited_by_count":360},{"id":"1706.02633","title":"Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs","created":"2017-06-08","cited_by_count":333},{"id":"1704.02510","title":"DualGAN: Unsupervised Dual Learning for Image-to-Image Translation","created":"2017-04-08","cited_by_count":310},{"id":"1904.01310","title":"DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis","created":"2019-04-02","cited_by_count":308},{"id":"2002.12478","title":"Time Series Data Augmentation for Deep Learning: A Survey","created":"2020-02-27","cited_by_count":226},{"id":"1802.04208","title":"Adversarial Audio Synthesis","created":"2018-02-12","cited_by_count":225},{"id":"1802.03446","title":"Pros and Cons of GAN Evaluation Measures","created":"2018-02-09","cited_by_count":224},{"id":"1709.05206","title":"LSTM Fully Convolutional Networks for Time Series Classification","created":"2017-09-08","cited_by_count":136},{"id":"1611.09904","title":"C-RNN-GAN: Continuous recurrent neural networks with adversarial training","created":"2016-11-29","cited_by_count":132},{"id":"1706.00527","title":"Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks","created":"2017-06-02","cited_by_count":131},{"id":"2007.15951","title":"An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks","created":"2020-07-31","cited_by_count":128},{"id":"2106.09296","title":"Voice2Series: Reprogramming Acoustic Models for Time Series Classification","created":"2021-06-17","cited_by_count":91},{"id":"1706.02071","title":"DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data","created":"2017-06-07","cited_by_count":77},{"id":"1806.01875","title":"EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals","created":"2018-06-05","cited_by_count":48},{"id":"2102.01331","title":"Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder","created":"2021-02-02","cited_by_count":39},{"id":"1808.02455","title":"Data augmentation using synthetic data for time series classification with deep residual networks","created":"2018-08-07","cited_by_count":38},{"id":"2008.08903","title":"Generative Adversarial Networks for Spatio-temporal Data: A Survey","created":"2020-08-18","cited_by_count":35},{"id":"2008.02663","title":"Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentation","created":"2020-08-06","cited_by_count":31},{"id":"1811.08295","title":"T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling","created":"2018-11-20","cited_by_count":29},{"id":"1707.06265","title":"Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation","created":"2017-07-19","cited_by_count":27},{"id":"2106.07085","title":"Survey: Image Mixing and Deleting for Data Augmentation","created":"2021-06-13","cited_by_count":26},{"id":"1904.00575","title":"A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series","created":"2019-04-01","cited_by_count":24},{"id":"1904.11685","title":"A Survey on Face Data Augmentation","created":"2019-04-26","cited_by_count":23},{"id":"2107.11098","title":"Generative adversarial networks in time series: A survey and taxonomy","created":"2021-07-23","cited_by_count":21},{"id":"1912.05509","title":"The Wasserstein-Fourier Distance for Stationary Time Series","created":"2019-12-11","cited_by_count":18},{"id":"1707.06799","title":"Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks","created":"2017-07-21","cited_by_count":17},{"id":"1910.04062","title":"DEVDAN: Deep Evolving Denoising Autoencoder","created":"2019-10-08","cited_by_count":16},{"id":"1910.05872","title":"Self-supervised Label Augmentation via Input Transformations","created":"2019-10-14","cited_by_count":15},{"id":"2203.11242","title":"A survey on GANs for computer vision: Recent research, analysis and taxonomy","created":"2022-03-21","cited_by_count":15},{"id":"1806.11382","title":"Convergence Problems with Generative Adversarial Networks (GANs)","created":"2018-06-29","cited_by_count":14},{"id":"1710.10182","title":"High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks","created":"2017-10-27","cited_by_count":11},{"id":"2105.04184","title":"Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation","created":"2021-05-10","cited_by_count":11},{"id":"2005.04188","title":"Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images","created":"2020-05-05","cited_by_count":9},{"id":"2103.09348","title":"Deep Time Series Models for Scarce Data","created":"2021-03-16","cited_by_count":8},{"id":"2107.14776","title":"Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks","created":"2021-07-30","cited_by_count":4},{"id":"2110.15914","title":"Improving the quality of generative models through Smirnov transformation","created":"2021-10-29","cited_by_count":1}],"next_cursor":null}