{"list":"other","total":51,"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":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"1503.02531","title":"Distilling the Knowledge in a Neural Network","created":"2015-03-09","cited_by_count":10324},{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"2104.14294","title":"Emerging Properties in Self-Supervised Vision Transformers","created":"2021-04-29","cited_by_count":6968},{"id":"1612.00796","title":"Overcoming catastrophic forgetting in neural networks","created":"2016-12-02","cited_by_count":4787},{"id":"1903.12261","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","created":"2019-03-28","cited_by_count":2877},{"id":"1606.09282","title":"Learning without Forgetting","created":"2016-06-29","cited_by_count":2496},{"id":"1611.07725","title":"iCaRL: Incremental Classifier and Representation Learning","created":"2016-11-23","cited_by_count":2333},{"id":"1311.3618","title":"Describing Textures in the Wild","created":"2013-11-14","cited_by_count":2125},{"id":"1706.08840","title":"Gradient Episodic Memory for Continual Learning","created":"2017-06-26","cited_by_count":1898},{"id":"1705.08690","title":"Continual Learning with Deep Generative Replay","created":"2017-05-24","cited_by_count":1158},{"id":"1812.00420","title":"Efficient Lifelong Learning with A-GEM","created":"2018-12-02","cited_by_count":1058},{"id":"1912.02781","title":"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty","created":"2019-12-05","cited_by_count":901},{"id":"2007.08501","title":"Accelerating 3D Deep Learning with PyTorch3D","created":"2020-07-16","cited_by_count":784},{"id":"1801.10112","title":"Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence","created":"2018-01-30","cited_by_count":742},{"id":"1905.13260","title":"Large Scale Incremental Learning","created":"2019-05-30","cited_by_count":732},{"id":"2008.08186","title":"Prevalence of Neural Collapse during the terminal phase of deep learning training","created":"2020-08-18","cited_by_count":719},{"id":"1807.09536","title":"End-to-End Incremental Learning","created":"2018-07-25","cited_by_count":597},{"id":"1611.06194","title":"Expert Gate: Lifelong Learning with a Network of Experts","created":"2016-11-18","cited_by_count":372},{"id":"2103.16788","title":"DER: Dynamically Expandable Representation for Class Incremental Learning","created":"2021-03-31","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":"1804.00175","title":"DeepIM: Deep Iterative Matching for 6D Pose Estimation","created":"2018-03-31","cited_by_count":283},{"id":"1612.00496","title":"3D Bounding Box Estimation Using Deep Learning and Geometry","created":"2016-12-01","cited_by_count":278},{"id":"2103.17230","title":"Rainbow Memory: Continual Learning with a Memory of Diverse Samples","created":"2021-03-31","cited_by_count":273},{"id":"2204.04662","title":"FOSTER: Feature Boosting and Compression for Class-Incremental Learning","created":"2022-04-10","cited_by_count":229},{"id":"1812.03596","title":"Task-Free Continual Learning","created":"2018-12-10","cited_by_count":221},{"id":"2002.10211","title":"Mnemonics Training: Multi-Class Incremental Learning without Forgetting","created":"2020-02-24","cited_by_count":221},{"id":"2010.05063","title":"Adaptive Aggregation Networks for Class-Incremental Learning","created":"2020-10-10","cited_by_count":149},{"id":"2211.13131","title":"FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning","created":"2022-11-23","cited_by_count":121},{"id":"2309.14062","title":"FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning","created":"2023-09-25","cited_by_count":87},{"id":"2104.08572","title":"On Learning the Geodesic Path for Incremental Learning","created":"2021-04-17","cited_by_count":85},{"id":"2112.12533","title":"PyCIL: A Python Toolbox for Class-Incremental Learning","created":"2021-12-23","cited_by_count":83},{"id":"2301.05792","title":"RMM: Reinforced Memory Management for Class-Incremental Learning","created":"2023-01-14","cited_by_count":66},{"id":"2104.00633","title":"RePOSE: Fast 6D Object Pose Refinement via Deep Texture Rendering","created":"2021-04-01","cited_by_count":63},{"id":"1411.6067","title":"Viewpoints and Keypoints","created":"2014-11-22","cited_by_count":59},{"id":"2005.11643","title":"Robust Object Detection under Occlusion with Context-Aware CompositionalNets","created":"2020-05-24","cited_by_count":54},{"id":"2303.14042","title":"Class-Incremental Exemplar Compression for Class-Incremental Learning","created":"2023-03-24","cited_by_count":50},{"id":"1803.09331","title":"StarMap for Category-Agnostic Keypoint and Viewpoint Estimation","created":"2018-03-25","cited_by_count":42},{"id":"2101.12378","title":"NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation","created":"2021-01-29","cited_by_count":41},{"id":"2006.15538","title":"Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion","created":"2020-06-28","cited_by_count":38},{"id":"2203.14952","title":"Energy-based Latent Aligner for Incremental Learning","created":"2022-03-28","cited_by_count":37},{"id":"2301.05032","title":"Online Hyperparameter Optimization for Class-Incremental Learning","created":"2023-01-11","cited_by_count":36},{"id":"2209.05624","title":"Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features","created":"2022-09-12","cited_by_count":23},{"id":"2205.15401","title":"VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesis","created":"2022-05-30","cited_by_count":22},{"id":"2110.14213","title":"Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose","created":"2021-10-27","cited_by_count":15},{"id":"2310.16115","title":"Wakening Past Concepts without Past Data: Class-Incremental Learning from Online Placebos","created":"2023-10-24","cited_by_count":14},{"id":"2305.14668","title":"NOVUM: Neural Object Volumes for Robust Object Classification","created":"2023-05-24","cited_by_count":11},{"id":"2306.00118","title":"Neural Textured Deformable Meshes for Robust Analysis-by-Synthesis","created":"2023-05-31","cited_by_count":4},{"id":"2208.06195","title":"Category-Level Pose Retrieval with Contrastive Features Learnt with Occlusion Augmentation","created":"2022-08-12","cited_by_count":2}],"next_cursor":null}