{"list":"other","total":51,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"1506.02142","title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning","created":"2015-06-06","cited_by_count":4448},{"id":"1708.07747","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","created":"2017-08-25","cited_by_count":4349},{"id":"1612.01474","title":"Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles","created":"2016-12-05","cited_by_count":3926},{"id":"1903.12261","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","created":"2019-03-28","cited_by_count":2877},{"id":"1610.02136","title":"A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks","created":"2016-10-07","cited_by_count":2385},{"id":"1703.04977","title":"What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?","created":"2017-03-15","cited_by_count":2352},{"id":"1505.05770","title":"Variational Inference with Normalizing Flows","created":"2015-05-21","cited_by_count":2170},{"id":"1609.04802","title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","created":"2016-09-15","cited_by_count":1908},{"id":"1706.02690","title":"Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks","created":"2017-06-08","cited_by_count":1328},{"id":"1406.2283","title":"Depth Map Prediction from a Single Image using a Multi-Scale Deep Network","created":"2014-06-09","cited_by_count":1274},{"id":"1910.09457","title":"Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods","created":"2019-10-21","cited_by_count":913},{"id":"2011.06225","title":"A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges","created":"2020-11-12","cited_by_count":819},{"id":"1806.01768","title":"Evidential Deep Learning to Quantify Classification Uncertainty","created":"2018-06-05","cited_by_count":732},{"id":"2011.14141","title":"AdaBins: Depth Estimation using Adaptive Bins","created":"2020-11-28","cited_by_count":601},{"id":"1802.10501","title":"Predictive Uncertainty Estimation via Prior Networks","created":"2018-02-28","cited_by_count":557},{"id":"1907.10326","title":"From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation","created":"2019-07-24","cited_by_count":382},{"id":"2002.06715","title":"BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning","created":"2020-02-17","cited_by_count":371},{"id":"1910.02600","title":"Deep Evidential Regression","created":"2019-10-07","cited_by_count":355},{"id":"1907.07484","title":"Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming","created":"2019-07-17","cited_by_count":336},{"id":"1708.06500","title":"Sparsity Invariant CNNs","created":"2017-08-22","cited_by_count":326},{"id":"1910.04851","title":"Addressing Failure Prediction by Learning Model Confidence","created":"2019-10-01","cited_by_count":186},{"id":"2005.06209","title":"On the uncertainty of self-supervised monocular depth estimation","created":"2020-05-13","cited_by_count":182},{"id":"2006.09239","title":"Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts","created":"2020-06-16","cited_by_count":164},{"id":"1901.07884","title":"Rank consistent ordinal regression for neural networks with application to age estimation","created":"2019-01-20","cited_by_count":133},{"id":"1708.01566","title":"Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes","created":"2017-08-04","cited_by_count":122},{"id":"1908.05005","title":"Benchmarking the Robustness of Semantic Segmentation Models","created":"2019-08-14","cited_by_count":111},{"id":"1905.13472","title":"Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness","created":"2019-05-31","cited_by_count":104},{"id":"2102.08501","title":"DEUP: Direct Epistemic Uncertainty Prediction","created":"2021-02-16","cited_by_count":104},{"id":"1605.02305","title":"Estimating Depth from Monocular Images as Classification Using Deep Fully Convolutional Residual Networks","created":"2016-05-08","cited_by_count":91},{"id":"1906.11537","title":"'In-Between' Uncertainty in Bayesian Neural Networks","created":"2019-06-27","cited_by_count":80},{"id":"2108.01634","title":"Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation","created":"2021-08-03","cited_by_count":40},{"id":"2002.07965","title":"Being Bayesian about Categorical Probability","created":"2020-02-19","cited_by_count":35},{"id":"2212.07359","title":"Post-hoc Uncertainty Learning using a Dirichlet Meta-Model","created":"2022-12-14","cited_by_count":31},{"id":"1910.04858","title":"Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate","created":"2019-09-28","cited_by_count":28},{"id":"2006.11590","title":"Regression Prior Networks","created":"2020-06-20","cited_by_count":28},{"id":"2208.02005","title":"Gradient-based Uncertainty for Monocular Depth Estimation","created":"2022-08-03","cited_by_count":26},{"id":"2103.10919","title":"Robustness via Cross-Domain Ensembles","created":"2021-03-19","cited_by_count":23},{"id":"2207.06873","title":"BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks","created":"2022-07-14","cited_by_count":23},{"id":"2006.15172","title":"A Comparison of Uncertainty Estimation Approaches in Deep Learning Components for Autonomous Vehicle Applications","created":"2020-06-26","cited_by_count":19},{"id":"2006.11584","title":"Calibration of Model Uncertainty for Dropout Variational Inference","created":"2020-06-20","cited_by_count":18},{"id":"2207.10130","title":"Latent Discriminant deterministic Uncertainty","created":"2022-07-20","cited_by_count":17},{"id":"2112.09368","title":"Improving evidential deep learning via multi-task learning","created":"2021-12-17","cited_by_count":13},{"id":"2110.11182","title":"SLURP: Side Learning Uncertainty for Regression Problems","created":"2021-10-21","cited_by_count":12},{"id":"2101.02447","title":"Practical Evaluation of Out-of-Distribution Detection Methods for Image Classification","created":"2021-01-07","cited_by_count":10},{"id":"2108.11505","title":"Generalized Real-World Super-Resolution through Adversarial Robustness","created":"2021-08-25","cited_by_count":9},{"id":"2206.05675","title":"A Survey on Uncertainty Reasoning and Quantification for Decision Making: Belief Theory Meets Deep Learning","created":"2022-06-12","cited_by_count":6},{"id":"2210.06776","title":"Improving the Reliability for Confidence Estimation","created":"2022-10-13","cited_by_count":6},{"id":"2202.12369","title":"On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression","created":"2022-02-24","cited_by_count":2}],"next_cursor":null}