{"list":"other","total":81,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"1503.02531","title":"Distilling the Knowledge in a Neural Network","created":"2015-03-09","cited_by_count":10324},{"id":"1506.01497","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","created":"2015-06-04","cited_by_count":9639},{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"1610.02391","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","created":"2016-10-07","cited_by_count":6733},{"id":"1608.06993","title":"Densely Connected Convolutional Networks","created":"2016-08-25","cited_by_count":6720},{"id":"1905.11946","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","created":"2019-05-28","cited_by_count":6442},{"id":"2203.00667","title":"Generative Adversarial Networks","created":"2022-03-01","cited_by_count":5627},{"id":"1512.00567","title":"Rethinking the Inception Architecture for Computer Vision","created":"2015-12-02","cited_by_count":5475},{"id":"1710.09412","title":"mixup: Beyond Empirical Risk Minimization","created":"2017-10-25","cited_by_count":5183},{"id":"1411.4038","title":"Fully Convolutional Networks for Semantic Segmentation","created":"2014-11-14","cited_by_count":4859},{"id":"1506.02640","title":"You Only Look Once: Unified, Real-Time Object Detection","created":"2015-06-08","cited_by_count":4852},{"id":"1709.01507","title":"Squeeze-and-Excitation Networks","created":"2017-09-05","cited_by_count":4789},{"id":"1711.00937","title":"Neural Discrete Representation Learning","created":"2017-11-02","cited_by_count":4712},{"id":"1409.4842","title":"Going Deeper with Convolutions","created":"2014-09-17","cited_by_count":4241},{"id":"1504.08083","title":"Fast R-CNN","created":"2015-04-30","cited_by_count":2933},{"id":"1909.13719","title":"RandAugment: Practical automated data augmentation with a reduced search space","created":"2019-09-30","cited_by_count":2269},{"id":"1311.2524","title":"Rich feature hierarchies for accurate object detection and semantic segmentation","created":"2013-11-11","cited_by_count":2162},{"id":"1308.3432","title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","created":"2013-08-15","cited_by_count":2079},{"id":"1603.05027","title":"Identity Mappings in Deep Residual Networks","created":"2016-03-16","cited_by_count":1965},{"id":"1806.09055","title":"DARTS: Differentiable Architecture Search","created":"2018-06-24","cited_by_count":1798},{"id":"1708.04552","title":"Improved Regularization of Convolutional Neural Networks with Cutout","created":"2017-08-15","cited_by_count":1628},{"id":"1611.01578","title":"Neural Architecture Search with Reinforcement Learning","created":"2016-11-05","cited_by_count":1499},{"id":"1708.04896","title":"Random Erasing Data Augmentation","created":"2017-08-16","cited_by_count":1239},{"id":"1611.00712","title":"The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables","created":"2016-11-02","cited_by_count":1148},{"id":"1802.01548","title":"Regularized Evolution for Image Classifier Architecture Search","created":"2018-02-05","cited_by_count":909},{"id":"1906.00910","title":"Learning Representations by Maximizing Mutual Information Across Views","created":"2019-06-03","cited_by_count":734},{"id":"1703.01780","title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","created":"2017-03-06","cited_by_count":472},{"id":"1904.05046","title":"Generalizing from a Few Examples: A Survey on Few-Shot Learning","created":"2019-04-10","cited_by_count":463},{"id":"1606.04586","title":"Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning","created":"2016-06-14","cited_by_count":441},{"id":"1712.04621","title":"The Effectiveness of Data Augmentation in Image Classification using Deep Learning","created":"2017-12-13","cited_by_count":370},{"id":"1711.09846","title":"Population Based Training of Neural Networks","created":"2017-11-27","cited_by_count":367},{"id":"1911.02590","title":"Optimizing Millions of Hyperparameters by Implicit Differentiation","created":"2019-11-06","cited_by_count":304},{"id":"1803.11364","title":"Joint Optimization Framework for Learning with Noisy Labels","created":"2018-03-30","cited_by_count":293},{"id":"1812.09926","title":"SNAS: Stochastic Neural Architecture Search","created":"2018-12-24","cited_by_count":289},{"id":"1911.09665","title":"Adversarial Examples Improve Image Recognition","created":"2019-11-21","cited_by_count":286},{"id":"1906.10652","title":"Monte Carlo Gradient Estimation in Machine Learning","created":"2019-06-25","cited_by_count":283},{"id":"1910.04465","title":"Searching for A Robust Neural Architecture in Four GPU Hours","created":"2019-10-10","cited_by_count":260},{"id":"1905.00397","title":"Fast AutoAugment","created":"2019-05-01","cited_by_count":243},{"id":"1703.01785","title":"Forward and Reverse Gradient-Based Hyperparameter Optimization","created":"2017-03-06","cited_by_count":234},{"id":"1903.07788","title":"Probabilistic End-to-end Noise Correction for Learning with Noisy Labels","created":"2019-03-19","cited_by_count":210},{"id":"1602.03409","title":"Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning","created":"2016-02-10","cited_by_count":209},{"id":"1807.11809","title":"Deep learning in agriculture: A survey","created":"2018-07-31","cited_by_count":160},{"id":"1905.05393","title":"Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules","created":"2019-05-14","cited_by_count":154},{"id":"1906.11172","title":"Learning Data Augmentation Strategies for Object Detection","created":"2019-06-26","cited_by_count":139},{"id":"1702.05538","title":"Dataset Augmentation in Feature Space","created":"2017-02-17","cited_by_count":124},{"id":"1912.11188","title":"Adversarial AutoAugment","created":"2019-12-24","cited_by_count":111},{"id":"1711.00123","title":"Backpropagation through the Void: Optimizing control variates for black-box gradient estimation","created":"2017-10-31","cited_by_count":106},{"id":"1803.06084","title":"A Kernel Theory of Modern Data Augmentation","created":"2018-03-16","cited_by_count":106},{"id":"1801.02929","title":"Data Augmentation by Pairing Samples for Images Classification","created":"2018-01-09","cited_by_count":96},{"id":"1911.06987","title":"Faster AutoAugment: Learning Augmentation Strategies using Backpropagation","created":"2019-11-16","cited_by_count":96},{"id":"1709.01643","title":"Learning to Compose Domain-Specific Transformations for Data Augmentation","created":"2017-09-06","cited_by_count":91},{"id":"2103.01592","title":"A Comprehensive Study on Face Recognition Biases Beyond Demographics","created":"2021-03-02","cited_by_count":86},{"id":"1803.07055","title":"Simple random search provides a competitive approach to reinforcement learning","created":"2018-03-19","cited_by_count":75},{"id":"1805.02641","title":"Label Refinery: Improving ImageNet Classification through Label Progression","created":"2018-05-07","cited_by_count":65},{"id":"2003.03780","title":"DADA: Differentiable Automatic Data Augmentation","created":"2020-03-08","cited_by_count":57},{"id":"2012.00152","title":"Every Model Learned by Gradient Descent Is Approximately a Kernel Machine","created":"2020-11-30","cited_by_count":55},{"id":"1703.08383","title":"Smart Augmentation - Learning an Optimal Data Augmentation Strategy","created":"2017-03-24","cited_by_count":54},{"id":"1710.10564","title":"A Bayesian Data Augmentation Approach for Learning Deep Models","created":"2017-10-29","cited_by_count":50},{"id":"1910.12795","title":"Learning Data Manipulation for Augmentation and Weighting","created":"2019-10-28","cited_by_count":45},{"id":"2002.08973","title":"Affinity and Diversity: Quantifying Mechanisms of Data Augmentation","created":"2020-02-20","cited_by_count":45},{"id":"1805.09707","title":"Jointly Optimize Data Augmentation and Network Training: Adversarial Data Augmentation in Human Pose Estimation","created":"2018-05-24","cited_by_count":44},{"id":"1704.03414","title":"A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection","created":"2017-04-11","cited_by_count":43},{"id":"1802.02375","title":"ShakeDrop Regularization for Deep Residual Learning","created":"2018-02-07","cited_by_count":40},{"id":"1905.07373","title":"Online Hyper-parameter Learning for Auto-Augmentation Strategy","created":"2019-05-17","cited_by_count":38},{"id":"2003.14348","title":"UniformAugment: A Search-free Probabilistic Data Augmentation Approach","created":"2020-03-31","cited_by_count":38},{"id":"1412.6071","title":"Fractional Max-Pooling","created":"2014-12-18","cited_by_count":35},{"id":"2003.00393","title":"Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision","created":"2020-03-01","cited_by_count":32},{"id":"2009.07724","title":"SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning","created":"2020-09-16","cited_by_count":32},{"id":"2003.11342","title":"Circumventing Outliers of AutoAugment with Knowledge Distillation","created":"2020-03-25","cited_by_count":29},{"id":"2009.14737","title":"Improving Auto-Augment via Augmentation-Wise Weight Sharing","created":"2020-09-30","cited_by_count":28},{"id":"1505.03229","title":"APAC: Augmented PAttern Classification with Neural Networks","created":"2015-05-13","cited_by_count":23},{"id":"2103.17220","title":"Scale-aware Automatic Augmentation for Object Detection","created":"2021-03-31","cited_by_count":23},{"id":"1909.12868","title":"Automatically Learning Data Augmentation Policies for Dialogue Tasks","created":"2019-09-27","cited_by_count":20},{"id":"2109.00523","title":"Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification","created":"2021-09-01","cited_by_count":15},{"id":"2103.05863","title":"AutoDO: Robust AutoAugment for Biased Data with Label Noise via Scalable Probabilistic Implicit Differentiation","created":"2021-03-10","cited_by_count":14},{"id":"2103.11099","title":"Local Patch AutoAugment with Multi-Agent Collaboration","created":"2021-03-20","cited_by_count":8},{"id":"1901.04420","title":"Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness","created":"2019-01-14","cited_by_count":7},{"id":"1908.00704","title":"Greedy AutoAugment","created":"2019-08-02","cited_by_count":4}],"next_cursor":null}