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awesome-active-learning
Awesome Active Learning Paper List
https://github.com/yongjin-shin/awesome-active-learning
Last synced: 1 day ago
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Papers
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Pool-Based Sampling
- A Variance Maximization Criterion for Active Learning
- Learning Algorithms for Active Learning
- Bayesian Generative Active Deep Learning - Toan Do, Ian Reid, Gustavo Carneiro. (ICML, 2019)
- Variational Adversarial Active Learning
- Entropic Open-Set Active Learning
- Class-Balanced Active Learning for Image Classification
- Influence Selection for Active Learning
- A Variance Maximization Criterion for Active Learning
- Learning Algorithms for Active Learning
- Beyond Disagreement-based Agnostic Active Learning
- Bayesian Optimal Active Search and Surveying
- Bayesian Active Learning for Classification and Prefernce Learning
- Active Learning using On-line Algorithms
- Active Learning from Crowds
- Stochastic Batch Acquisition for Deep Active Learning - Charron, Yarin Gal. (arXiv, 2021)
- LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active Learning - Yeong Kim, Kyungwoo Song, JoonHo Jang, Il-chul Moon. (NeurIPS, 2021)
- Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision
- Deep Batch Active Learning By Diverse, Uncertain Gradient Lower Bound
- Bayesian Generative Active Deep Learning - Toan Do, Ian Reid, Gustavo Carneiro. (ICML, 2019)
- Learning Loss for Active Learning
- Variational Adversarial Active Learning
- Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning
- Rapid Performance Gain through Active Model Reuse - Feng Li. (IJCAI, 2019)
- Active Semi-Supervised Learning Using Sampling Theory for Graph Signals
- Active Learning for Multi-Objective Optimization
- Querying Discriminative and Representative Samples forBatch Mode Active Learning
- Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization
- Active Learning for Probabilistic Hypotheses Usingthe Maximum Gibbs Error Criterion
- Batch Active Learning via Coordinated Matching
- Ask me better questions: active learning queries based on rule induction
- Active Instance Sampling via Matrix Partition
- Discriminative Batch Mode Active Learning
- Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision
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Stream-Based Selective Sampling
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Meta-Learning
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