{"list":"other","total":81,"rows":[{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"1812.04948","title":"A Style-Based Generator Architecture for Generative Adversarial Networks","created":"2018-12-12","cited_by_count":6013},{"id":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"id":"1912.04958","title":"Analyzing and Improving the Image Quality of StyleGAN","created":"2019-12-03","cited_by_count":3588},{"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":"1809.11096","title":"Large Scale GAN Training for High Fidelity Natural Image Synthesis","created":"2018-09-28","cited_by_count":2626},{"id":"1510.00149","title":"Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding","created":"2015-10-01","cited_by_count":2329},{"id":"1803.03635","title":"The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks","created":"2018-03-09","cited_by_count":2262},{"id":"1704.00028","title":"Improved Training of Wasserstein GANs","created":"2017-03-31","cited_by_count":2235},{"id":"1511.06434","title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","created":"2015-11-19","cited_by_count":2054},{"id":"1802.05957","title":"Spectral Normalization for Generative Adversarial Networks","created":"2018-02-16","cited_by_count":1636},{"id":"1901.08573","title":"Theoretically Principled Trade-off between Robustness and Accuracy","created":"2019-01-24","cited_by_count":1517},{"id":"2006.10029","title":"Big Self-Supervised Models are Strong Semi-Supervised Learners","created":"2020-06-17","cited_by_count":1380},{"id":"2006.06676","title":"Training Generative Adversarial Networks with Limited Data","created":"2020-06-11","cited_by_count":1186},{"id":"1611.01236","title":"Adversarial Machine Learning at Scale","created":"2016-11-04","cited_by_count":936},{"id":"1805.12152","title":"Robustness May Be at Odds with Accuracy","created":"2018-05-30","cited_by_count":880},{"id":"1805.08318","title":"Self-Attention Generative Adversarial Networks","created":"2018-05-21","cited_by_count":809},{"id":"1801.04406","title":"Which Training Methods for GANs do actually Converge?","created":"2018-01-13","cited_by_count":679},{"id":"1708.06519","title":"Learning Efficient Convolutional Networks through Network Slimming","created":"2017-08-22","cited_by_count":676},{"id":"1912.05671","title":"Linear Mode Connectivity and the Lottery Ticket Hypothesis","created":"2019-12-11","cited_by_count":637},{"id":"1810.05270","title":"Rethinking the Value of Network Pruning","created":"2018-10-11","cited_by_count":599},{"id":"1612.03242","title":"StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks","created":"2016-12-10","cited_by_count":577},{"id":"1902.09574","title":"The State of Sparsity in Deep Neural Networks","created":"2019-02-25","cited_by_count":476},{"id":"2002.07376","title":"Picking Winning Tickets Before Training by Preserving Gradient Flow","created":"2020-02-18","cited_by_count":471},{"id":"1906.06972","title":"EnlightenGAN: Deep Light Enhancement without Paired Supervision","created":"2019-06-17","cited_by_count":466},{"id":"1701.04862","title":"Towards Principled Methods for Training Generative Adversarial Networks","created":"2017-01-17","cited_by_count":464},{"id":"2006.10738","title":"Differentiable Augmentation for Data-Efficient GAN Training","created":"2020-06-18","cited_by_count":363},{"id":"1908.03826","title":"DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better","created":"2019-08-10","cited_by_count":362},{"id":"2006.06195","title":"Large-Scale Adversarial Training for Vision-and-Language Representation Learning","created":"2020-06-11","cited_by_count":327},{"id":"1711.04043","title":"Few-Shot Learning with Graph Neural Networks","created":"2017-11-10","cited_by_count":324},{"id":"1804.11285","title":"Adversarially Robust Generalization Requires More Data","created":"2018-04-30","cited_by_count":318},{"id":"1612.07828","title":"Learning from Simulated and Unsupervised Images through Adversarial Training","created":"2016-12-22","cited_by_count":288},{"id":"1911.09665","title":"Adversarial Examples Improve Image Recognition","created":"2019-11-21","cited_by_count":286},{"id":"1711.04340","title":"Data Augmentation Generative Adversarial Networks","created":"2017-11-12","cited_by_count":278},{"id":"2007.12223","title":"The Lottery Ticket Hypothesis for Pre-trained BERT Networks","created":"2020-07-23","cited_by_count":275},{"id":"1909.11764","title":"FreeLB: Enhanced Adversarial Training for Natural Language Understanding","created":"2019-09-25","cited_by_count":264},{"id":"1705.08584","title":"MMD GAN: Towards Deeper Understanding of Moment Matching Network","created":"2017-05-24","cited_by_count":254},{"id":"1802.05637","title":"cGANs with Projection Discriminator","created":"2018-02-15","cited_by_count":254},{"id":"2003.02389","title":"Comparing Rewinding and Fine-tuning in Neural Network Pruning","created":"2020-03-05","cited_by_count":240},{"id":"1506.05751","title":"Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks","created":"2015-06-18","cited_by_count":214},{"id":"2001.06937","title":"A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications","created":"2020-01-20","cited_by_count":209},{"id":"1812.00740","title":"Disentangling Adversarial Robustness and Generalization","created":"2018-12-03","cited_by_count":172},{"id":"1909.11957","title":"Drawing Early-Bird Tickets: Towards More Efficient Training of Deep Networks","created":"2019-09-26","cited_by_count":160},{"id":"2003.12862","title":"Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning","created":"2020-03-28","cited_by_count":153},{"id":"2010.13337","title":"Robust Pre-Training by Adversarial Contrastive Learning","created":"2020-10-26","cited_by_count":144},{"id":"2101.04775","title":"Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis","created":"2021-01-12","cited_by_count":140},{"id":"1910.12027","title":"Consistency Regularization for Generative Adversarial Networks","created":"2019-10-26","cited_by_count":136},{"id":"1805.01677","title":"Transferring GANs: generating images from limited data","created":"2018-05-04","cited_by_count":134},{"id":"1906.06032","title":"Adversarial Training Can Hurt Generalization","created":"2019-06-14","cited_by_count":132},{"id":"2002.10964","title":"Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs","created":"2020-02-25","cited_by_count":132},{"id":"2012.02780","title":"Few-shot Image Generation with Elastic Weight Consolidation","created":"2020-12-04","cited_by_count":122},{"id":"2102.06790","title":"A Unified Lottery Ticket Hypothesis for Graph Neural Networks","created":"2021-02-12","cited_by_count":118},{"id":"2002.10365","title":"The Early Phase of Neural Network Training","created":"2020-02-24","cited_by_count":115},{"id":"1912.05270","title":"MineGAN: effective knowledge transfer from GANs to target domains with few images","created":"2019-12-11","cited_by_count":106},{"id":"1904.01774","title":"Image Generation From Small Datasets via Batch Statistics Adaptation","created":"2019-04-03","cited_by_count":102},{"id":"1912.04427","title":"Winning the Lottery with Continuous Sparsification","created":"2019-12-10","cited_by_count":100},{"id":"1811.11212","title":"Self-Supervised GANs via Auxiliary Rotation Loss","created":"2018-11-27","cited_by_count":94},{"id":"1908.03835","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","created":"2019-08-11","cited_by_count":94},{"id":"2012.06908","title":"The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models","created":"2020-12-12","cited_by_count":91},{"id":"2101.00063","title":"EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets","created":"2020-12-31","cited_by_count":79},{"id":"1906.02768","title":"Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP","created":"2019-06-06","cited_by_count":73},{"id":"2002.04724","title":"Improved Consistency Regularization for GANs","created":"2020-02-11","cited_by_count":69},{"id":"2010.11943","title":"Few-Shot Adaptation of Generative Adversarial Networks","created":"2020-10-22","cited_by_count":64},{"id":"1906.10732","title":"The Difficulty of Training Sparse Neural Networks","created":"2019-06-25","cited_by_count":62},{"id":"1910.04284","title":"Improved Sample Complexities for Deep Networks and Robust Classification via an All-Layer Margin","created":"2019-10-09","cited_by_count":56},{"id":"2006.10728","title":"Diverse Image Generation via Self-Conditioned GANs","created":"2020-06-18","cited_by_count":55},{"id":"1903.02271","title":"High-Fidelity Image Generation With Fewer Labels","created":"2019-03-06","cited_by_count":52},{"id":"1906.03805","title":"Improving Neural Language Modeling via Adversarial Training","created":"2019-06-10","cited_by_count":49},{"id":"2010.11828","title":"Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free","created":"2020-10-22","cited_by_count":48},{"id":"2002.10025","title":"Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference","created":"2020-02-24","cited_by_count":45},{"id":"1910.13540","title":"Small-GAN: Speeding Up GAN Training Using Core-sets","created":"2019-10-29","cited_by_count":41},{"id":"1901.00532","title":"Adversarial Robustness May Be at Odds With Simplicity","created":"2019-01-02","cited_by_count":39},{"id":"2003.03519","title":"Distilling portable Generative Adversarial Networks for Image Translation","created":"2020-03-07","cited_by_count":37},{"id":"2006.05338","title":"On Data Augmentation for GAN Training","created":"2020-06-09","cited_by_count":25},{"id":"2010.02350","title":"Winning Lottery Tickets in Deep Generative Models","created":"2020-10-05","cited_by_count":25},{"id":"1910.12708","title":"Evaluating Lottery Tickets Under Distributional Shifts","created":"2019-10-28","cited_by_count":23},{"id":"2007.15255","title":"Instance Selection for GANs","created":"2020-07-30","cited_by_count":23},{"id":"2002.01136","title":"On Positive-Unlabeled Classification in GAN","created":"2020-02-04","cited_by_count":14},{"id":"1902.11019","title":"Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors","created":"2019-02-28","cited_by_count":10}],"next_cursor":null}