{"list":"other","total":55,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"1911.05722","title":"Momentum Contrast for Unsupervised Visual Representation Learning","created":"2019-11-13","cited_by_count":7513},{"id":"2111.06377","title":"Masked Autoencoders Are Scalable Vision Learners","created":"2021-11-11","cited_by_count":7403},{"id":"2104.14294","title":"Emerging Properties in Self-Supervised Vision Transformers","created":"2021-04-29","cited_by_count":6968},{"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":"1311.3618","title":"Describing Textures in the Wild","created":"2013-11-14","cited_by_count":2125},{"id":"1605.08803","title":"Density estimation using Real NVP","created":"2016-05-27","cited_by_count":2042},{"id":"1807.03039","title":"Glow: Generative Flow with Invertible 1x1 Convolutions","created":"2018-07-09","cited_by_count":1545},{"id":"1807.03888","title":"A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks","created":"2018-07-10","cited_by_count":1485},{"id":"1907.07174","title":"Natural Adversarial Examples","created":"2019-07-16","cited_by_count":1408},{"id":"1506.03365","title":"LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop","created":"2015-06-10","cited_by_count":1355},{"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":"2010.03759","title":"Energy-based Out-of-distribution Detection","created":"2020-10-08","cited_by_count":1299},{"id":"2110.04544","title":"CLIP-Adapter: Better Vision-Language Models with Feature Adapters","created":"2021-10-09","cited_by_count":1026},{"id":"1812.04606","title":"Deep Anomaly Detection with Outlier Exposure","created":"2018-12-11","cited_by_count":942},{"id":"2111.07832","title":"iBOT: Image BERT Pre-Training with Online Tokenizer","created":"2021-11-15","cited_by_count":903},{"id":"2110.11334","title":"Generalized Out-of-Distribution Detection: A Survey","created":"2021-10-21","cited_by_count":802},{"id":"1705.07057","title":"Masked Autoregressive Flow for Density Estimation","created":"2017-05-19","cited_by_count":684},{"id":"2204.06507","title":"Out-of-Distribution Detection with Deep Nearest Neighbors","created":"2022-04-13","cited_by_count":600},{"id":"2111.12797","title":"ReAct: Out-of-distribution Detection With Rectified Activations","created":"2021-11-24","cited_by_count":486},{"id":"1911.11132","title":"Scaling Out-of-Distribution Detection for Real-World Settings","created":"2019-11-25","cited_by_count":465},{"id":"2002.11297","title":"Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data","created":"2020-02-26","cited_by_count":408},{"id":"2203.10807","title":"ViM: Out-Of-Distribution with Virtual-logit Matching","created":"2022-03-21","cited_by_count":405},{"id":"1711.09325","title":"Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples","created":"2017-11-26","cited_by_count":389},{"id":"2110.00218","title":"On the Importance of Gradients for Detecting Distributional Shifts in the Wild","created":"2021-10-01","cited_by_count":339},{"id":"2210.07242","title":"OpenOOD: Benchmarking Generalized Out-of-Distribution Detection","created":"2022-10-13","cited_by_count":268},{"id":"2205.09310","title":"Mitigating Neural Network Overconfidence with Logit Normalization","created":"2022-05-19","cited_by_count":252},{"id":"1802.04865","title":"Learning Confidence for Out-of-Distribution Detection in Neural Networks","created":"2018-02-13","cited_by_count":245},{"id":"2105.01879","title":"MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space","created":"2021-05-05","cited_by_count":233},{"id":"2007.05566","title":"Contrastive Training for Improved Out-of-Distribution Detection","created":"2020-07-10","cited_by_count":186},{"id":"1504.06755","title":"TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking","created":"2015-04-25","cited_by_count":165},{"id":"1811.04110","title":"Reducing Network Agnostophobia","created":"2018-11-09","cited_by_count":165},{"id":"2003.02977","title":"Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder","created":"2020-03-06","cited_by_count":145},{"id":"1809.03576","title":"Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers","created":"2018-09-04","cited_by_count":126},{"id":"2204.05306","title":"Full-Spectrum Out-of-Distribution Detection","created":"2022-04-11","cited_by_count":63},{"id":"2202.08088","title":"Latent Outlier Exposure for Anomaly Detection with Contaminated Data","created":"2022-02-16","cited_by_count":48},{"id":"2104.11408","title":"Neural Mean Discrepancy for Efficient Out-of-Distribution Detection","created":"2021-04-23","cited_by_count":42},{"id":"2006.04183","title":"Uncertainty-Aware Deep Classifiers using Generative Models","created":"2020-06-07","cited_by_count":41},{"id":"2203.14911","title":"Expanding Low-Density Latent Regions for Open-Set Object Detection","created":"2022-03-28","cited_by_count":38},{"id":"2109.09607","title":"Description of Corner Cases in Automated Driving: Goals and Challenges","created":"2021-09-20","cited_by_count":35},{"id":"2008.04872","title":"A Boundary Based Out-of-Distribution Classifier for Generalized Zero-Shot Learning","created":"2020-08-09","cited_by_count":34},{"id":"1912.12510","title":"Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices","created":"2019-12-28","cited_by_count":32},{"id":"2006.13265","title":"Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders","created":"2020-06-23","cited_by_count":30},{"id":"2102.12967","title":"A statistical framework for efficient out of distribution detection in deep neural networks","created":"2021-02-25","cited_by_count":28},{"id":"2106.15792","title":"Learning Bounds for Open-Set Learning","created":"2021-06-30","cited_by_count":27},{"id":"2203.09064","title":"Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning","created":"2022-03-17","cited_by_count":27},{"id":"2110.09246","title":"Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection","created":"2021-10-18","cited_by_count":23},{"id":"2201.13279","title":"UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs","created":"2022-01-31","cited_by_count":16},{"id":"2110.15811","title":"CVAD: A generic medical anomaly detector based on Cascade VAE","created":"2021-10-29","cited_by_count":6}],"next_cursor":null}