{"total":82,"rows":[{"id":"2211.14238","title":"Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time","created":"2022-11-25","cited_by_count":90,"area":null},{"id":"2206.09059","title":"CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks","created":"2022-06-18","cited_by_count":68,"area":null},{"id":"2210.03114","title":"CLIP model is an Efficient Continual Learner","created":"2022-10-06","cited_by_count":65,"area":null},{"id":"2303.11165","title":"Computationally Budgeted Continual Learning: What Does Matter?","created":"2023-03-20","cited_by_count":61,"area":null},{"id":"2302.01047","title":"Real-Time Evaluation in Online Continual Learning: A New Hope","created":"2023-02-02","cited_by_count":47,"area":null},{"id":"2202.05780","title":"A Modern Self-Referential Weight Matrix That Learns to Modify Itself","created":"2022-02-11","cited_by_count":41,"area":null},{"id":"2310.16226","title":"TiC-CLIP: Continual Training of CLIP Models","created":"2023-10-24","cited_by_count":38,"area":null},{"id":"2307.04345","title":"Continual Learning as Computationally Constrained Reinforcement Learning","created":"2023-07-10","cited_by_count":37,"area":null},{"id":"2210.03482","title":"CLAD: A realistic Continual Learning benchmark for Autonomous Driving","created":"2022-10-07","cited_by_count":36,"area":null},{"id":"2305.09253","title":"Online Continual Learning Without the Storage Constraint","created":"2023-05-16","cited_by_count":29,"area":null},{"id":"2209.09125","title":"Operationalizing Machine Learning: An Interview Study","created":"2022-09-16","cited_by_count":25,"area":null},{"id":"2308.14816","title":"CLNeRF: Continual Learning Meets NeRF","created":"2023-08-28","cited_by_count":24,"area":null},{"id":"2408.14471","title":"A Practitioner's Guide to Continual Multimodal Pretraining","created":"2024-08-26","cited_by_count":23,"area":null},{"id":"2412.06712","title":"How to Merge Your Multimodal Models Over Time?","created":"2024-12-09","cited_by_count":23,"area":null},{"id":"2203.06514","title":"Sparsity and Heterogeneous Dropout for Continual Learning in the Null Space of Neural Activations","created":"2022-03-12","cited_by_count":19,"area":null},{"id":"2211.11747","title":"NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research","created":"2022-11-15","cited_by_count":18,"area":null},{"id":"2303.08207","title":"Is forgetting less a good inductive bias for forward transfer?","created":"2023-03-14","cited_by_count":18,"area":null},{"id":"2310.16931","title":"CL-MASR: A Continual Learning Benchmark for Multilingual ASR","created":"2023-10-25","cited_by_count":17,"area":null},{"id":"2311.04898","title":"Two Complementary Perspectives to Continual Learning: Ask Not Only What to Optimize, But Also How","created":"2023-11-08","cited_by_count":16,"area":null},{"id":"2306.08448","title":"Kalman Filter for Online Classification of Non-Stationary Data","created":"2023-06-14","cited_by_count":15,"area":null},{"id":"2312.09651","title":"What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection","created":"2023-12-15","cited_by_count":15,"area":null},{"id":"2210.04993","title":"Continual Learning with Evolving Class Ontologies","created":"2022-10-10","cited_by_count":13,"area":null},{"id":"2210.07931","title":"Sequential Learning Of Neural Networks for Prequential MDL","created":"2022-10-14","cited_by_count":13,"area":null},{"id":"2402.09542","title":"Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning","created":"2024-02-14","cited_by_count":13,"area":null},{"id":"2407.16067","title":"LCA-on-the-Line: Benchmarking Out-of-Distribution Generalization with Class Taxonomies","created":"2024-07-22","cited_by_count":12,"area":null},{"id":"2409.19245","title":"Forgetting, Ignorance or Myopia: Revisiting Key Challenges in Online Continual Learning","created":"2024-09-28","cited_by_count":12,"area":null},{"id":"2503.10111","title":"Continual Text-to-Video Retrieval with Frame Fusion and Task-Aware Routing","created":"2025-03-13","cited_by_count":12,"area":null},{"id":"2206.15476","title":"AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection","created":"2022-06-30","cited_by_count":11,"area":null},{"id":"2308.03300","title":"Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio Detection","created":"2023-08-07","cited_by_count":11,"area":null},{"id":"2312.00598","title":"Learning from One Continuous Video Stream","created":"2023-12-01","cited_by_count":11,"area":null},{"id":"2504.13822","title":"Parameter-Efficient Continual Fine-Tuning: A Survey","created":"2025-04-18","cited_by_count":11,"area":null},{"id":"2305.09275","title":"Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right?","created":"2023-05-16","cited_by_count":10,"area":null},{"id":"2305.18418","title":"Just a Glimpse: Rethinking Temporal Information for Video Continual Learning","created":"2023-05-28","cited_by_count":10,"area":null},{"id":"2406.13123","title":"ViLCo-Bench: VIdeo Language COntinual learning Benchmark","created":"2024-06-19","cited_by_count":9,"area":null},{"id":"2504.01961","title":"Learning from Streaming Video with Orthogonal Gradients","created":"2025-04-02","cited_by_count":9,"area":null},{"id":"2211.14568","title":"BeGin: Extensive Benchmark Scenarios and An Easy-to-use Framework for Graph Continual Learning","created":"2022-11-26","cited_by_count":8,"area":null},{"id":"2401.15275","title":"Dynamic Transformer Architecture for Continual Learning of Multimodal Tasks","created":"2024-01-27","cited_by_count":8,"area":null},{"id":"2502.01866","title":"Online Curvature-Aware Replay: Leveraging $\\mathbf{2^{nd}}$ Order Information for Online Continual Learning","created":"2025-02-03","cited_by_count":8,"area":null},{"id":"2303.11076","title":"From MNIST to ImageNet and Back: Benchmarking Continual Curriculum Learning","created":"2023-03-16","cited_by_count":6,"area":null},{"id":"2410.13754","title":"MixEval-X: Any-to-Any Evaluations from Real-World Data Mixtures","created":"2024-10-17","cited_by_count":6,"area":null},{"id":"2205.11473","title":"Rethinking Streaming Machine Learning Evaluation","created":"2022-05-23","cited_by_count":5,"area":null},{"id":"2210.01422","title":"Time-Varying Propensity Score to Bridge the Gap between the Past and Present","created":"2022-10-04","cited_by_count":5,"area":null},{"id":"2211.16234","title":"SimCS: Simulation for Domain Incremental Online Continual Segmentation","created":"2022-11-29","cited_by_count":5,"area":null},{"id":"2311.11293","title":"From Categories to Classifiers: Name-Only Continual Learning by Exploring the Web","created":"2023-11-19","cited_by_count":5,"area":null},{"id":"2412.11551","title":"Region-Based Optimization in Continual Learning for Audio Deepfake Detection","created":"2024-12-16","cited_by_count":5,"area":null},{"id":"2501.08737","title":"Resource-Constrained Federated Continual Learning: What Does Matter?","created":"2025-01-15","cited_by_count":5,"area":null},{"id":"2206.11736","title":"NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds","created":"2022-06-23","cited_by_count":4,"area":null},{"id":"2405.06816","title":"Non-stationary Domain Generalization: Theory and Algorithm","created":"2024-05-10","cited_by_count":4,"area":null},{"id":"2503.12053","title":"Ferret: An Efficient Online Continual Learning Framework under Varying Memory Constraints","created":"2025-03-15","cited_by_count":4,"area":null},{"id":"2210.03103","title":"Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!","created":"2022-10-06","cited_by_count":3,"area":null},{"id":"2210.06401","title":"Improving information retention in large scale online continual learning","created":"2022-10-12","cited_by_count":3,"area":null},{"id":"2212.05908","title":"Instance-Conditional Timescales of Decay for Non-Stationary Learning","created":"2022-12-12","cited_by_count":3,"area":null},{"id":"2503.18511","title":"Global Convergence of Continual Learning on Non-IID Data","created":"2025-03-24","cited_by_count":3,"area":null},{"id":"2504.12503","title":"Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study","created":"2025-04-16","cited_by_count":3,"area":null},{"id":"2512.04571","title":"Temp-SCONE: A Novel Out-of-Distribution Detection and Domain Generalization Framework for Wild Data with Temporal Shift","created":"2025-12-04","cited_by_count":3,"area":null},{"id":"2209.06855","title":"Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications","created":"2022-09-14","cited_by_count":2,"area":null},{"id":"2306.07716","title":"Dynamically Masked Discriminator for Generative Adversarial Networks","created":"2023-06-13","cited_by_count":2,"area":null},{"id":"2308.12366","title":"Continual Zero-Shot Learning through Semantically Guided Generative Random Walks","created":"2023-08-23","cited_by_count":2,"area":null},{"id":"2310.15974","title":"Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees","created":"2023-10-24","cited_by_count":2,"area":null},{"id":"2404.10758","title":"Watch Your Step: Optimal Retrieval for Continual Learning at Scale","created":"2024-04-16","cited_by_count":2,"area":null},{"id":"2404.17930","title":"Multi-Stream Cellular Test-Time Adaptation of Real-Time Models Evolving in Dynamic Environments","created":"2024-04-27","cited_by_count":2,"area":null},{"id":"2407.00176","title":"The impact of model size on catastrophic forgetting in Online Continual Learning","created":"2024-06-28","cited_by_count":2,"area":null},{"id":"2412.18790","title":"Torque-Aware Momentum","created":"2024-12-25","cited_by_count":2,"area":null},{"id":"2503.04655","title":"CLDyB: Towards Dynamic Benchmarking for Continual Learning with Pre-trained Models","created":"2025-03-06","cited_by_count":2,"area":null},{"id":"2507.00534","title":"NIRANTAR: Continual Learning with New Languages and Domains on Real-world Speech Data","created":"2025-07-01","cited_by_count":2,"area":null},{"id":"2507.02310","title":"Holistic Continual Learning under Concept Drift with Adaptive Memory Realignment","created":"2025-07-03","cited_by_count":2,"area":null},{"id":"2603.01677","title":"A Practical Guide to Streaming Continual Learning","created":"2026-03-02","cited_by_count":2,"area":null},{"id":"2307.05784","title":"EgoAdapt: A multi-stream evaluation study of adaptation to real-world egocentric user video","created":"2023-07-11","cited_by_count":1,"area":null},{"id":"2404.03084","title":"Rethinking Teacher-Student Curriculum Learning through the Cooperative Mechanics of Experience","created":"2024-04-03","cited_by_count":1,"area":null},{"id":"2604.21766","title":"AUDITA: A New Dataset to Audit Humans vs. AI Skill at Audio QA","created":"2026-04-23","cited_by_count":1,"area":null},{"id":"2406.07590","title":"StreamFP: Learnable Fingerprint-guided Data Selection for Efficient Stream Learning","created":"2024-06-11","cited_by_count":0,"area":null},{"id":"2407.09642","title":"Seq-to-Final: A Benchmark for Tuning from Sequential Distributions to a Final Time Point","created":"2024-07-12","cited_by_count":0,"area":null},{"id":"2508.03868","title":"Prediction-Oriented Subsampling from Data Streams","created":"2025-08-05","cited_by_count":0,"area":null},{"id":"2510.17626","title":"CaMiT: A Time-Aware Car Model Dataset for Classification and Generation","created":"2025-10-20","cited_by_count":0,"area":null},{"id":"2603.00253","title":"CoPeP: Benchmarking Continual Pretraining for Protein Language Models","created":"2026-02-27","cited_by_count":0,"area":null},{"id":"2604.01116","title":"ProTPS: Prototype-Guided Text Prompt Selection for Continual Learning","created":"2026-04-01","cited_by_count":0,"area":null},{"id":"2604.10586","title":"Preventing Latent Rehearsal Decay in Online Continual SSL with SOLAR","created":"2026-04-12","cited_by_count":0,"area":null},{"id":"2605.19080","title":"MANGO: Meta-Adaptive Network Gradient Optimization for Online Continual Learning","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2606.14900","title":"GRASP: Gradient-Aligned Sequential Parameter Transfer for Memory-Efficient Multi-Source Learning","created":"2026-06-12","cited_by_count":0,"area":null},{"id":"2607.08784","title":"HERO: A Heterogeneity-Aware Benchmark Library for Federated Continual Learning","created":"2026-06-13","cited_by_count":0,"area":null},{"id":"2607.18289","title":"Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios","created":"2026-06-30","cited_by_count":0,"area":null},{"id":"2610.16923","title":"Minimax Optimality of Shampoo for Nonstationary Stochastic Optimization","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}