{"list":"other","total":56,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"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":"2304.02643","title":"Segment Anything","created":"2023-04-05","cited_by_count":8234},{"id":"2304.07193","title":"DINOv2: Learning Robust Visual Features without Supervision","created":"2023-04-14","cited_by_count":7974},{"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":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"id":"1605.07146","title":"Wide Residual Networks","created":"2016-05-23","cited_by_count":3070},{"id":"1903.12261","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","created":"2019-03-28","cited_by_count":2877},{"id":"1611.05431","title":"Aggregated Residual Transformations for Deep Neural Networks","created":"2016-11-16","cited_by_count":2546},{"id":"2106.01946","title":"Convex optimization","created":"2021-06-03","cited_by_count":2515},{"id":"2003.01690","title":"Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks","created":"2020-03-03","cited_by_count":1451},{"id":"2002.08546","title":"Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation","created":"2020-02-20","cited_by_count":848},{"id":"1705.07204","title":"Ensemble Adversarial Training: Attacks and Defenses","created":"2017-05-19","cited_by_count":840},{"id":"1708.06131","title":"Evasion Attacks against Machine Learning at Test Time","created":"2017-08-21","cited_by_count":689},{"id":"1708.03999","title":"ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models","created":"2017-08-14","cited_by_count":666},{"id":"2203.13591","title":"Continual Test-Time Domain Adaptation","created":"2022-03-25","cited_by_count":525},{"id":"1804.00792","title":"Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks","created":"2018-04-03","cited_by_count":519},{"id":"1908.02983","title":"Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning","created":"2019-08-08","cited_by_count":510},{"id":"1804.08598","title":"Black-box Adversarial Attacks with Limited Queries and Information","created":"2018-04-23","cited_by_count":501},{"id":"2204.02610","title":"Efficient Test-Time Model Adaptation without Forgetting","created":"2022-04-06","cited_by_count":469},{"id":"1801.00553","title":"Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey","created":"2018-01-02","cited_by_count":460},{"id":"2302.12400","title":"Towards Stable Test-Time Adaptation in Dynamic Wild World","created":"2023-02-24","cited_by_count":415},{"id":"2110.09506","title":"MEMO: Test Time Robustness via Adaptation and Augmentation","created":"2021-10-18","cited_by_count":377},{"id":"2303.15361","title":"A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts","created":"2023-03-27","cited_by_count":359},{"id":"1810.00069","title":"Adversarial Attacks and Defences: A Survey","created":"2018-09-28","cited_by_count":259},{"id":"2101.04898","title":"Unlearnable Examples: Making Personal Data Unexploitable","created":"2021-01-13","cited_by_count":193},{"id":"2303.13899","title":"Robust Test-Time Adaptation in Dynamic Scenarios","created":"2023-03-24","cited_by_count":189},{"id":"2208.05117","title":"NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation","created":"2022-08-10","cited_by_count":151},{"id":"2303.01904","title":"EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularization","created":"2023-03-03","cited_by_count":123},{"id":"2106.10807","title":"Adversarial Examples Make Strong Poisons","created":"2021-06-21","cited_by_count":119},{"id":"2005.14137","title":"QEBA: Query-Efficient Boundary-Based Blackbox Attack","created":"2020-05-28","cited_by_count":104},{"id":"2211.13081","title":"Robust Mean Teacher for Continual and Gradual Test-Time Adaptation","created":"2022-11-23","cited_by_count":104},{"id":"2210.04831","title":"Visual Prompt Tuning for Test-time Domain Adaptation","created":"2022-10-10","cited_by_count":97},{"id":"2306.00650","title":"Universal Test-time Adaptation through Weight Ensembling, Diversity Weighting, and Prior Correction","created":"2023-06-01","cited_by_count":65},{"id":"1703.01340","title":"Generative Poisoning Attack Method Against Neural Networks","created":"2017-03-03","cited_by_count":52},{"id":"2210.03885","title":"Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts","created":"2022-10-08","cited_by_count":50},{"id":"2212.09713","title":"A Probabilistic Framework for Lifelong Test-Time Adaptation","created":"2022-12-19","cited_by_count":46},{"id":"2206.02721","title":"Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering","created":"2022-06-06","cited_by_count":44},{"id":"2104.12928","title":"If your data distribution shifts, use self-learning","created":"2021-04-27","cited_by_count":36},{"id":"2308.09942","title":"On the Robustness of Open-World Test-Time Training: Self-Training with Dynamic Prototype Expansion","created":"2023-08-19","cited_by_count":34},{"id":"2309.14949","title":"Towards Real-World Test-Time Adaptation: Tri-Net Self-Training with Balanced Normalization","created":"2023-09-26","cited_by_count":34},{"id":"2312.10165","title":"Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization","created":"2023-12-15","cited_by_count":31},{"id":"2103.11576","title":"Grey-box Adversarial Attack And Defence For Sentiment Classification","created":"2021-03-22","cited_by_count":28},{"id":"2409.13401","title":"PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images","created":"2024-09-20","cited_by_count":24},{"id":"2405.02797","title":"Adapting to Distribution Shift by Visual Domain Prompt Generation","created":"2024-05-05","cited_by_count":23},{"id":"2303.10856","title":"Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training","created":"2023-03-20","cited_by_count":19},{"id":"1906.03181","title":"POBA-GA: Perturbation Optimized Black-Box Adversarial Attacks via Genetic Algorithm","created":"2019-05-01","cited_by_count":14},{"id":"2303.17937","title":"STFAR: Improving Object Detection Robustness at Test-Time by Self-Training with Feature Alignment Regularization","created":"2023-03-31","cited_by_count":13},{"id":"2407.12128","title":"Distribution Alignment for Fully Test-Time Adaptation with Dynamic Online Data Streams","created":"2024-07-16","cited_by_count":13},{"id":"2301.12576","title":"Uncovering Adversarial Risks of Test-Time Adaptation","created":"2023-01-29","cited_by_count":12},{"id":"2104.15064","title":"Black-box adversarial attacks using Evolution Strategies","created":"2021-04-30","cited_by_count":11},{"id":"2403.19326","title":"MedBN: Robust Test-Time Adaptation against Malicious Test Samples","created":"2024-03-28","cited_by_count":10},{"id":"2110.14735","title":"Towards Evaluating the Robustness of Neural Networks Learned by Transduction","created":"2021-10-27","cited_by_count":8},{"id":"2308.08505","title":"Test-Time Poisoning Attacks Against Test-Time Adaptation Models","created":"2023-08-16","cited_by_count":7}],"next_cursor":null}