{"list":"other","total":51,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1503.02531","title":"Distilling the Knowledge in a Neural Network","created":"2015-03-09","cited_by_count":10324},{"id":"1704.04861","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","created":"2017-04-17","cited_by_count":3845},{"id":"1411.7766","title":"Deep Learning Face Attributes in the Wild","created":"2014-11-28","cited_by_count":3693},{"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":"1506.02626","title":"Learning both Weights and Connections for Efficient Neural Networks","created":"2015-06-08","cited_by_count":2031},{"id":"1608.08710","title":"Pruning Filters for Efficient ConvNets","created":"2016-08-31","cited_by_count":1143},{"id":"1810.02340","title":"SNIP: Single-shot Network Pruning based on Connection Sensitivity","created":"2018-10-04","cited_by_count":812},{"id":"1707.06168","title":"Channel Pruning for Accelerating Very Deep Neural Networks","created":"2017-07-19","cited_by_count":702},{"id":"1708.06519","title":"Learning Efficient Convolutional Networks through Network Slimming","created":"2017-08-22","cited_by_count":676},{"id":"2003.03033","title":"What is the State of Neural Network Pruning?","created":"2020-03-06","cited_by_count":583},{"id":"1906.10771","title":"Importance Estimation for Neural Network Pruning","created":"2019-06-25","cited_by_count":541},{"id":"2002.07376","title":"Picking Winning Tickets Before Training by Preserving Gradient Flow","created":"2020-02-18","cited_by_count":471},{"id":"1702.08423","title":"Age Progression/Regression by Conditional Adversarial Autoencoder","created":"2017-02-27","cited_by_count":429},{"id":"1707.09457","title":"Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints","created":"2017-07-29","cited_by_count":420},{"id":"1710.09282","title":"A Survey of Model Compression and Acceleration for Deep Neural Networks","created":"2017-10-23","cited_by_count":301},{"id":"1911.11834","title":"Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation","created":"2019-11-26","cited_by_count":252},{"id":"1711.05908","title":"NISP: Pruning Networks using Neuron Importance Score Propagation","created":"2017-11-16","cited_by_count":228},{"id":"1910.02806","title":"Learning De-biased Representations with Biased Representations","created":"2019-10-07","cited_by_count":199},{"id":"1911.05248","title":"What Do Compressed Deep Neural Networks Forget?","created":"2019-11-13","cited_by_count":171},{"id":"2010.03058","title":"Characterising Bias in Compressed Models","created":"2020-10-06","cited_by_count":167},{"id":"1909.11957","title":"Drawing Early-Bird Tickets: Towards More Efficient Training of Deep Networks","created":"2019-09-26","cited_by_count":160},{"id":"1904.07911","title":"REPAIR: Removing Representation Bias by Dataset Resampling","created":"2019-04-16","cited_by_count":157},{"id":"1912.07726","title":"Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy","created":"2019-12-16","cited_by_count":156},{"id":"1801.02385","title":"Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification","created":"2018-01-08","cited_by_count":136},{"id":"2012.01469","title":"Fair Attribute Classification through Latent Space De-biasing","created":"2020-12-02","cited_by_count":125},{"id":"1908.08843","title":"Fairness in Deep Learning: A Computational Perspective","created":"2019-08-23","cited_by_count":123},{"id":"1803.04311","title":"Deep Learning in Mobile and Wireless Networking: A Survey","created":"2018-03-12","cited_by_count":118},{"id":"2107.01372","title":"Learning Debiased Representation via Disentangled Feature Augmentation","created":"2021-07-03","cited_by_count":116},{"id":"1809.02169","title":"Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings","created":"2018-09-06","cited_by_count":114},{"id":"2106.08503","title":"Understanding and Evaluating Racial Biases in Image Captioning","created":"2021-06-16","cited_by_count":108},{"id":"1511.06067","title":"Convolutional neural networks with low-rank regularization","created":"2015-11-19","cited_by_count":103},{"id":"2104.02821","title":"Towards Measuring Fairness in AI: the Casual Conversations Dataset","created":"2021-04-06","cited_by_count":80},{"id":"1901.08668","title":"Guarantees for Spectral Clustering with Fairness Constraints","created":"2019-01-24","cited_by_count":72},{"id":"1512.06974","title":"Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels","created":"2015-12-22","cited_by_count":64},{"id":"2108.08504","title":"Understanding and Mitigating Annotation Bias in Facial Expression Recognition","created":"2021-08-19","cited_by_count":50},{"id":"2106.05519","title":"Consistent Instance False Positive Improves Fairness in Face Recognition","created":"2021-06-10","cited_by_count":40},{"id":"2111.14581","title":"Learning Fair Classifiers with Partially Annotated Group Labels","created":"2021-11-29","cited_by_count":40},{"id":"2005.10430","title":"Gender Slopes: Counterfactual Fairness for Computer Vision Models by Attribute Manipulation","created":"2020-05-21","cited_by_count":37},{"id":"1906.09688","title":"Transfer of Machine Learning Fairness across Domains","created":"2019-06-24","cited_by_count":36},{"id":"2206.07173","title":"Measuring Representational Harms in Image Captioning","created":"2022-06-14","cited_by_count":36},{"id":"2004.01019","title":"Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition","created":"2020-04-02","cited_by_count":31},{"id":"1807.09810","title":"Coreset-Based Neural Network Compression","created":"2018-07-25","cited_by_count":27},{"id":"2009.11491","title":"Understanding Fairness of Gender Classification Algorithms Across Gender-Race Groups","created":"2020-09-24","cited_by_count":24},{"id":"2109.03229","title":"Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets","created":"2021-09-07","cited_by_count":22},{"id":"2005.07302","title":"Investigating Bias in Deep Face Analysis: The KANFace Dataset and Empirical Study","created":"2020-05-15","cited_by_count":18},{"id":"2106.07849","title":"Simon Says: Evaluating and Mitigating Bias in Pruned Neural Networks with Knowledge Distillation","created":"2021-06-15","cited_by_count":15},{"id":"1906.10337","title":"COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning","created":"2019-06-25","cited_by_count":14},{"id":"2201.01709","title":"The Effect of Model Compression on Fairness in Facial Expression Recognition","created":"2022-01-05","cited_by_count":13},{"id":"2204.04601","title":"Explaining Deep Convolutional Neural Networks via Latent Visual-Semantic Filter Attention","created":"2022-04-10","cited_by_count":11}],"next_cursor":null}