{"list":"other","total":61,"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":"2106.09685","title":"LoRA: Low-Rank Adaptation of Large Language Models","created":"2021-06-17","cited_by_count":14380},{"id":"1503.02531","title":"Distilling the Knowledge in a Neural Network","created":"2015-03-09","cited_by_count":10324},{"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":"1706.04599","title":"On Calibration of Modern Neural Networks","created":"2017-06-14","cited_by_count":4960},{"id":"1709.01507","title":"Squeeze-and-Excitation Networks","created":"2017-09-05","cited_by_count":4789},{"id":"1604.01685","title":"The Cityscapes Dataset for Semantic Urban Scene Understanding","created":"2016-04-06","cited_by_count":4444},{"id":"1902.00751","title":"Parameter-Efficient Transfer Learning for NLP","created":"2019-02-02","cited_by_count":4236},{"id":"1802.02611","title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","created":"2018-02-07","cited_by_count":3223},{"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":"1807.06521","title":"CBAM: Convolutional Block Attention Module","created":"2018-07-17","cited_by_count":2477},{"id":"1706.08840","title":"Gradient Episodic Memory for Continual Learning","created":"2017-06-26","cited_by_count":1898},{"id":"1706.02677","title":"Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour","created":"2017-06-08","cited_by_count":1492},{"id":"2007.01434","title":"In Search of Lost Domain Generalization","created":"2020-07-02","cited_by_count":1054},{"id":"1906.02629","title":"When Does Label Smoothing Help?","created":"2019-06-06","cited_by_count":988},{"id":"1912.02781","title":"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty","created":"2019-12-05","cited_by_count":901},{"id":"2010.09670","title":"RobustBench: a standardized adversarial robustness benchmark","created":"2020-10-19","cited_by_count":661},{"id":"1811.12833","title":"ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation","created":"2018-11-30","cited_by_count":623},{"id":"2104.02008","title":"Domain Generalization with MixStyle","created":"2021-04-05","cited_by_count":566},{"id":"2203.13591","title":"Continual Test-Time Domain Adaptation","created":"2022-03-25","cited_by_count":525},{"id":"1908.02983","title":"Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning","created":"2019-08-08","cited_by_count":510},{"id":"2204.02610","title":"Efficient Test-Time Model Adaptation without Forgetting","created":"2022-04-06","cited_by_count":469},{"id":"2006.16971","title":"Improving robustness against common corruptions by covariate shift adaptation","created":"2020-06-30","cited_by_count":431},{"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":"1807.09441","title":"Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net","created":"2018-07-25","cited_by_count":364},{"id":"1602.04433","title":"Unsupervised Domain Adaptation with Residual Transfer Networks","created":"2016-02-14","cited_by_count":341},{"id":"1812.02391","title":"Meta-Transfer Learning for Few-Shot Learning","created":"2018-12-06","cited_by_count":306},{"id":"2007.10319","title":"MCUNet: Tiny Deep Learning on IoT Devices","created":"2020-07-20","cited_by_count":273},{"id":"2204.10377","title":"Contrastive Test-Time Adaptation","created":"2022-04-21","cited_by_count":261},{"id":"2006.10963","title":"Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift","created":"2020-06-19","cited_by_count":236},{"id":"2103.15597","title":"RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening","created":"2021-03-29","cited_by_count":215},{"id":"2209.07522","title":"Test-Time Training with Masked Autoencoders","created":"2022-09-15","cited_by_count":212},{"id":"2008.12197","title":"Instance Adaptive Self-Training for Unsupervised Domain Adaptation","created":"2020-08-27","cited_by_count":177},{"id":"2104.05833","title":"Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization","created":"2021-04-12","cited_by_count":141},{"id":"2202.03958","title":"Uncertainty Modeling for Out-of-Distribution Generalization","created":"2022-02-08","cited_by_count":125},{"id":"2203.07740","title":"Exact Feature Distribution Matching for Arbitrary Style Transfer and Domain Generalization","created":"2022-03-15","cited_by_count":122},{"id":"2206.02916","title":"Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks","created":"2022-06-06","cited_by_count":104},{"id":"2302.05155","title":"TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation","created":"2023-02-10","cited_by_count":101},{"id":"2204.12667","title":"MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation","created":"2022-04-27","cited_by_count":76},{"id":"2106.14999","title":"Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformation","created":"2021-06-28","cited_by_count":68},{"id":"2207.11707","title":"Improving Test-Time Adaptation via Shift-agnostic Weight Regularization and Nearest Source Prototypes","created":"2022-07-24","cited_by_count":67},{"id":"2204.03838","title":"Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptation","created":"2022-04-08","cited_by_count":66},{"id":"1911.00623","title":"On-Device Machine Learning: An Algorithms and Learning Theory Perspective","created":"2019-11-02","cited_by_count":63},{"id":"2012.04828","title":"Two-phase Pseudo Label Densification for Self-training based Domain Adaptation","created":"2020-12-09","cited_by_count":60},{"id":"2112.02355","title":"SITA: Single Image Test-time Adaptation","created":"2021-12-04","cited_by_count":59},{"id":"2110.04065","title":"Test-time Batch Statistics Calibration for Covariate Shift","created":"2021-10-06","cited_by_count":50},{"id":"2110.11478","title":"MixNorm: Test-Time Adaptation Through Online Normalization Estimation","created":"2021-10-21","cited_by_count":47},{"id":"2108.05570","title":"LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation","created":"2021-08-12","cited_by_count":33},{"id":"2110.12369","title":"AuxAdapt: Stable and Efficient Test-Time Adaptation for Temporally Consistent Video Semantic Segmentation","created":"2021-10-24","cited_by_count":31},{"id":"2110.04111","title":"Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation","created":"2021-10-08","cited_by_count":24},{"id":"2206.00205","title":"CAFA: Class-Aware Feature Alignment for Test-Time Adaptation","created":"2022-06-01","cited_by_count":21},{"id":"2012.01362","title":"$DA^3$:Dynamic Additive Attention Adaption for Memory-EfficientOn-Device Multi-Domain Learning","created":"2020-12-02","cited_by_count":6},{"id":"2302.14611","title":"TransAdapt: A Transformative Framework for Online Test Time Adaptive Semantic Segmentation","created":"2023-02-24","cited_by_count":6},{"id":"2208.08767","title":"Evaluating Continual Test-Time Adaptation for Contextual and Semantic Domain Shifts","created":"2022-08-18","cited_by_count":5},{"id":"2212.06242","title":"Test-time Adaptation vs. Training-time Generalization: A Case Study in Human Instance Segmentation using Keypoints Estimation","created":"2022-12-12","cited_by_count":5},{"id":"2212.08356","title":"Test-time Adaptation in the Dynamic World with Compound Domain Knowledge Management","created":"2022-12-16","cited_by_count":5}],"next_cursor":null}