{"total":101,"rows":[{"id":"2110.11325","title":"Learning 3D Semantic Segmentation with only 2D Image Supervision","created":"2021-10-21","cited_by_count":60,"area":null},{"id":"2405.00334","title":"A Survey on Deep Active Learning: Recent Advances and New Frontiers","created":"2024-05-01","cited_by_count":54,"area":null},{"id":"2112.01001","title":"SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency","created":"2021-12-02","cited_by_count":48,"area":null},{"id":"2207.02261","title":"OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning","created":"2022-07-05","cited_by_count":37,"area":null},{"id":"2410.05952","title":"Active Evaluation Acquisition for Efficient LLM Benchmarking","created":"2024-10-08","cited_by_count":35,"area":null},{"id":"2305.01975","title":"A Survey on Dataset Distillation: Approaches, Applications and Future Directions","created":"2023-05-03","cited_by_count":32,"area":null},{"id":"2107.03176","title":"On Training Instance Selection for Few-Shot Neural Text Generation","created":"2021-07-07","cited_by_count":26,"area":null},{"id":"2112.05683","title":"Boosting Active Learning via Improving Test Performance","created":"2021-12-10","cited_by_count":19,"area":null},{"id":"2302.13960","title":"Acquisition Conditioned Oracle for Nongreedy Active Feature Acquisition","created":"2023-02-27","cited_by_count":19,"area":null},{"id":"2311.04414","title":"Learning the What and How of Annotation in Video Object Segmentation","created":"2023-11-08","cited_by_count":19,"area":null},{"id":"2302.07317","title":"Algorithm Selection for Deep Active Learning with Imbalanced Datasets","created":"2023-02-14","cited_by_count":18,"area":null},{"id":"2205.13340","title":"Deep Active Learning with Noise Stability","created":"2022-05-26","cited_by_count":14,"area":null},{"id":"2304.04220","title":"Towards Active Learning for Action Spotting in Association Football Videos","created":"2023-04-09","cited_by_count":14,"area":null},{"id":"2404.16804","title":"AAPL: Adding Attributes to Prompt Learning for Vision-Language Models","created":"2024-04-25","cited_by_count":14,"area":"mllm"},{"id":"2112.09245","title":"Automated Deep Learning: Neural Architecture Search Is Not the End","created":"2021-12-16","cited_by_count":13,"area":null},{"id":"2404.04631","title":"On the Limitations of Large Language Models (LLMs): False Attribution","created":"2024-04-06","cited_by_count":13,"area":null},{"id":"2312.04064","title":"DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design","created":"2023-12-07","cited_by_count":12,"area":null},{"id":"2205.02564","title":"One Size Does Not Fit All: The Case for Personalised Word Complexity Models","created":"2022-05-05","cited_by_count":10,"area":null},{"id":"2205.09995","title":"Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning","created":"2022-05-20","cited_by_count":10,"area":null},{"id":"2107.01622","title":"Multiple-criteria Based Active Learning with Fixed-size Determinantal Point Processes","created":"2021-07-04","cited_by_count":9,"area":null},{"id":"2107.02378","title":"Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks","created":"2021-07-06","cited_by_count":9,"area":null},{"id":"2211.10013","title":"Active Learning by Query by Committee with Robust Divergences","created":"2022-11-18","cited_by_count":9,"area":null},{"id":"2305.13342","title":"On the Limitations of Simulating Active Learning","created":"2023-05-21","cited_by_count":9,"area":null},{"id":"2402.16515","title":"LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classification","created":"2024-02-26","cited_by_count":8,"area":"distill"},{"id":"2209.01858","title":"Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification","created":"2022-09-05","cited_by_count":7,"area":null},{"id":"2104.01836","title":"Stopping Criterion for Active Learning Based on Error Stability","created":"2021-04-05","cited_by_count":6,"area":null},{"id":"2106.13516","title":"Multi-Domain Active Learning: Literature Review and Comparative Study","created":"2021-06-25","cited_by_count":6,"area":null},{"id":"2204.10905","title":"Label a Herd in Minutes: Individual Holstein-Friesian Cattle Identification","created":"2022-04-22","cited_by_count":6,"area":null},{"id":"2209.12756","title":"FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering","created":"2022-09-21","cited_by_count":6,"area":null},{"id":"2304.11762","title":"You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic Segmentation","created":"2023-04-23","cited_by_count":6,"area":null},{"id":"2507.08749","title":"Modeling Partially Observed Nonlinear Dynamical Systems and Efficient Data Assimilation via Discrete-Time Conditional Gaussian Koopman Network","created":"2025-07-11","cited_by_count":6,"area":null},{"id":"2106.06933","title":"Active Learning for Network Traffic Classification: A Technical Study","created":"2021-06-13","cited_by_count":5,"area":null},{"id":"2201.10774","title":"Competition over data: how does data purchase affect users?","created":"2022-01-26","cited_by_count":5,"area":null},{"id":"2203.04507","title":"Onception: Active Learning with Expert Advice for Real World Machine Translation","created":"2022-03-09","cited_by_count":5,"area":null},{"id":"2302.11075","title":"Deep Active Learning in the Presence of Label Noise: A Survey","created":"2023-02-22","cited_by_count":5,"area":null},{"id":"2101.09933","title":"A Survey on Active Deep Learning: From Model-driven to Data-driven","created":"2021-01-25","cited_by_count":4,"area":null},{"id":"2110.07509","title":"TDACNN: Target-domain-free Domain Adaptation Convolutional Neural Network for Drift Compensation in Gas Sensors","created":"2021-10-14","cited_by_count":4,"area":null},{"id":"2304.13032","title":"A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction","created":"2023-04-06","cited_by_count":4,"area":null},{"id":"2306.00765","title":"Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection","created":"2023-06-01","cited_by_count":4,"area":null},{"id":"2306.08954","title":"An Expanded Benchmark that Rediscovers and Affirms the Edge of Uncertainty Sampling for Active Learning in Tabular Datasets","created":"2023-06-15","cited_by_count":4,"area":null},{"id":"2407.06001","title":"Pseudo-triplet Guided Few-shot Composed Image Retrieval","created":"2024-07-08","cited_by_count":4,"area":null},{"id":"2108.07670","title":"ImitAL: Learning Active Learning Strategies from Synthetic Data","created":"2021-08-17","cited_by_count":3,"area":null},{"id":"2112.02611","title":"Multi-View Active Learning for Short Text Classification in User-Generated Data","created":"2021-12-05","cited_by_count":3,"area":null},{"id":"2201.02304","title":"Budget-aware Few-shot Learning via Graph Convolutional Network","created":"2022-01-07","cited_by_count":3,"area":null},{"id":"2206.13391","title":"Deep reinforced active learning for multi-class image classification","created":"2022-06-20","cited_by_count":3,"area":null},{"id":"2207.01190","title":"Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios","created":"2022-07-04","cited_by_count":3,"area":null},{"id":"2307.00968","title":"REAL: A Representative Error-Driven Approach for Active Learning","created":"2023-07-03","cited_by_count":3,"area":null},{"id":"2310.17044","title":"Learning to Rank for Active Learning via Multi-Task Bilevel Optimization","created":"2023-10-25","cited_by_count":3,"area":null},{"id":"2410.03915","title":"Distribution Guided Active Feature Acquisition","created":"2024-10-04","cited_by_count":3,"area":null},{"id":"2508.13983","title":"OmViD: Omni-supervised active learning for video action detection","created":"2025-08-19","cited_by_count":3,"area":null},{"id":"2510.23524","title":"Toward Carbon-Neutral Human AI: Rethinking Data, Computation, and Learning Paradigms for Sustainable Intelligence","created":"2025-10-27","cited_by_count":3,"area":null},{"id":"2201.07200","title":"Optimizing Active Learning for Low Annotation Budgets","created":"2022-01-18","cited_by_count":2,"area":null},{"id":"2205.11117","title":"PyRelationAL: a python library for active learning research and development","created":"2022-05-23","cited_by_count":2,"area":null},{"id":"2405.15031","title":"Amortized nonmyopic active search via deep imitation learning","created":"2024-05-23","cited_by_count":2,"area":"offlinerl"},{"id":"2405.17734","title":"Towards Efficient Disaster Response via Cost-effective Unbiased Class Rate Estimation through Neyman Allocation Stratified Sampling Active Learning","created":"2024-05-28","cited_by_count":2,"area":null},{"id":"2411.05939","title":"GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise","created":"2024-11-08","cited_by_count":2,"area":null},{"id":"2506.07805","title":"Representative, Informative, and De-Amplifying: Requirements for Robust Bayesian Active Learning under Model Misspecification","created":"2025-06-09","cited_by_count":2,"area":null},{"id":"2508.01460","title":"Uncertainty-Aware Segmentation Quality Prediction via Deep Learning Bayesian Modeling: Comprehensive Evaluation and Interpretation on Skin Cancer and Liver Segmentation","created":"2025-08-02","cited_by_count":2,"area":null},{"id":"2208.11636","title":"ImitAL: Learned Active Learning Strategy on Synthetic Data","created":"2022-08-24","cited_by_count":1,"area":null},{"id":"2309.05477","title":"Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes","created":"2023-09-11","cited_by_count":1,"area":null},{"id":"2311.02879","title":"Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling","created":"2023-11-06","cited_by_count":1,"area":null},{"id":"2312.16965","title":"Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection","created":"2023-12-28","cited_by_count":1,"area":null},{"id":"2402.06056","title":"ActiveDP: Bridging Active Learning and Data Programming","created":"2024-02-08","cited_by_count":1,"area":null},{"id":"2406.19054","title":"A look under the hood of the Interactive Deep Learning Enterprise (No-IDLE)","created":"2024-06-27","cited_by_count":1,"area":null},{"id":"2501.00277","title":"Efficient Human-in-the-Loop Active Learning: A Novel Framework for Data Labeling in AI Systems","created":"2024-12-31","cited_by_count":1,"area":null},{"id":"2505.23451","title":"A Reverse Causal Framework to Mitigate Spurious Correlations for Debiasing Scene Graph Generation","created":"2025-05-29","cited_by_count":1,"area":null},{"id":"2101.07561","title":"Leveraging Local Variation in Data: Sampling and Weighting Schemes for Supervised Deep Learning","created":"2021-01-19","cited_by_count":0,"area":null},{"id":"2102.11267","title":"Interpret-able feedback for AutoML systems","created":"2021-02-22","cited_by_count":0,"area":null},{"id":"2110.03765","title":"Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-Supervised Learning","created":"2021-10-07","cited_by_count":0,"area":null},{"id":"2205.00387","title":"Crude Oil-related Events Extraction and Processing: A Transfer Learning Approach","created":"2022-05-01","cited_by_count":0,"area":null},{"id":"2212.04868","title":"Frugal Reinforcement-based Active Learning","created":"2022-12-09","cited_by_count":0,"area":null},{"id":"2212.13974","title":"Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection","created":"2022-12-28","cited_by_count":0,"area":null},{"id":"2307.08782","title":"Unsupervised Learning of Distributional Properties can Supplement Human Labeling and Increase Active Learning Efficiency in Anomaly Detection","created":"2023-07-13","cited_by_count":0,"area":null},{"id":"2308.14904","title":"Maturity-Aware Active Learning for Semantic Segmentation with Hierarchically-Adaptive Sample Assessment","created":"2023-08-28","cited_by_count":0,"area":null},{"id":"2309.14781","title":"Frugal Satellite Image Change Detection with Deep-Net Inversion","created":"2023-09-26","cited_by_count":0,"area":null},{"id":"2311.10278","title":"Physics-Enhanced Multi-fidelity Learning for Optical Surface Imprint","created":"2023-11-17","cited_by_count":0,"area":null},{"id":"2311.18356","title":"Towards Comparable Active Learning","created":"2023-11-30","cited_by_count":0,"area":null},{"id":"2404.07306","title":"AI-Guided Defect Detection Techniques to Model Single Crystal Diamond Growth","created":"2024-04-10","cited_by_count":0,"area":null},{"id":"2404.08017","title":"AI-Guided Feature Segmentation Techniques to Model Features from Single Crystal Diamond Growth","created":"2024-04-10","cited_by_count":0,"area":null},{"id":"2404.09953","title":"Classification Tree-based Active Learning: A Wrapper Approach","created":"2024-04-15","cited_by_count":0,"area":null},{"id":"2406.04261","title":"Simulating, Fast and Slow: Learning Policies for Black-Box Optimization","created":"2024-06-06","cited_by_count":0,"area":null},{"id":"2407.17992","title":"Amortized Active Learning for Nonparametric Functions","created":"2024-07-25","cited_by_count":0,"area":null},{"id":"2506.03817","title":"Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Grid","created":"2025-06-04","cited_by_count":0,"area":null},{"id":"2507.22434","title":"RANA: Robust Active Learning for Noisy Network Alignment","created":"2025-07-30","cited_by_count":0,"area":null},{"id":"2509.15805","title":"Boosting Active Learning with Knowledge Transfer","created":"2025-09-19","cited_by_count":0,"area":null},{"id":"2509.24181","title":"Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification","created":"2025-09-29","cited_by_count":0,"area":null},{"id":"2510.06926","title":"Label-frugal satellite image change detection with generative virtual exemplar learning","created":"2025-10-08","cited_by_count":0,"area":null},{"id":"2510.18660","title":"Image augmentation with invertible networks in interactive satellite image change detection","created":"2025-10-21","cited_by_count":0,"area":null},{"id":"2511.01015","title":"What's the next frontier for Data-centric AI? Data Savvy Agents","created":"2025-11-02","cited_by_count":0,"area":null},{"id":"2511.03367","title":"Decoupling Augmentation Bias in Prompt Learning for Vision-Language Models","created":"2025-11-05","cited_by_count":0,"area":"mllm"},{"id":"2512.17100","title":"UniCoMTE: A Universal Counterfactual Framework for Explaining Time-Series Classifiers on ECG Data","created":"2025-12-18","cited_by_count":0,"area":null},{"id":"2512.17398","title":"DeepShare: Sharing ReLU Across Channels and Layers for Efficient Private Inference","created":"2025-12-19","cited_by_count":0,"area":null},{"id":"2512.20021","title":"Gaussian Process Assisted Meta-learning for Image Classification and Object Detection Models","created":"2025-12-23","cited_by_count":0,"area":null},{"id":"2512.21152","title":"MODE: Multi-Objective Adaptive Coreset Selection","created":"2025-12-24","cited_by_count":0,"area":null},{"id":"2601.05946","title":"A Critical Examination of Active Learning Workflows in Materials Science","created":"2026-01-09","cited_by_count":0,"area":null},{"id":"2603.10435","title":"Adaptive Active Learning for Regression via Reinforcement Learning","created":"2026-03-11","cited_by_count":0,"area":"deeprl"},{"id":"2603.11370","title":"Relaxed Efficient Acquisition of Context and Temporal Features","created":"2026-03-11","cited_by_count":0,"area":null},{"id":"2604.03505","title":"Multimodal Urban Tree Detection from Satellite and Street-Level Imagery via Annotation-Efficient Deep Learning Strategies","created":"2026-04-03","cited_by_count":0,"area":null},{"id":"2606.00141","title":"Adaptive data selection improves wearable prediction under low baseline performance","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2606.07910","title":"CAAL: Contextual Bandits based Online Hand-Craft Active Learning Strategy Selection","created":"2026-06-06","cited_by_count":0,"area":null}],"next_cursor":"100"}