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Section Navigation

  • 🏊 Pool-based Active Learning: Classification
    • Core Set
    • Random Sampling
    • MaxHerding
    • Active Learning with Cost Embedding (ALCE)
    • Density-Diversity-Distribution-Distance Sampling (4DS)
    • Uncertainty Herding (UHerding)
    • Contrastive Active Learning (CAL)
    • Typical Clustering (TypiClust)
    • Querying Informative and Representative Examples (QUIRE)
    • Uncertainty Sampling (US) with Entropy
    • Epistemic Uncertainty Sampling (EpisUS)
    • Uncertainty Sampling (US) with Margin
    • Uncertainty Sampling (US) with Least-Confidence
    • Probability Coverage (ProbCover)
    • Uncertainty Sampling with Expected Average Precision (USAP)
    • Monte-Carlo Expected Error Reduction (EER) with Log-Loss
    • Query-by-Committee (QBC) with Vote Entropy
    • Query-by-Committee (QBC) with Variation Ratios
    • Monte-Carlo Expected Error Reduction (EER) with Misclassification-Loss
    • Sub-sampling Wrapper
    • Discriminative Active Learning (DAL)
    • Parallel Utility Estimation Wrapper
    • Multi-class Probabilistic Active Learning (McPAL)
    • Query-by-Committee (QBC) with Kullback-Leibler Divergence
    • Value of Information (VOI)
    • Value of Information (VOI) on Labeled Samples
    • Value of Information (VOI) on Unlabeled Samples
    • Density-weighted Uncertainty Sampling (DWUS)
    • Bayesian Active Learning by Disagreement (BALD)
    • Dual Strategy for Active Learning
    • Batch Active Learning by Diverse Gradient Embedding (BADGE)
    • Clustering Uncertainty-weighted Embeddings (CLUE)
    • Dropout Query (DropQuery)
    • Batch Density-Diversity-Distribution-Distance Sampling (4DS)
    • Fast Active Learning by Contrastive UNcertainty (FALCUN)
    • Batch Bayesian Active Learning by Disagreement (BatchBALD)
    • Label Cardinality Inconsistency (LCI)
    • Maximum Loss Reduction with Maximal Confidence (MMC)
  • 🏊 Pool-based Active Learning: Regression
    • Greedy Sampling on the Feature Space (GSx)
    • Greedy Sampling on the Target Space (GSy)
    • Improved Greedy Sampling (GSi)
    • Expected Model Variance Reduction
    • Expected Model Change Maximization
    • Query-by-Committee (QBC) with Empirical Variance
    • Expected Model Output Change
    • Regression based Kullback Leibler Divergence Maximization
    • Regression Tree Based Active Learning (RT-AL) with Random Selection
    • Regression Tree Based Active Learning (RT-AL) with Diversity Selection
    • Regression Tree Based Active Learning (RT-AL) with Representativity Selection
  • 🏊 Pool-based Active Learning: Multi-annotator Learning
    • Interval Estimation Threshold
    • Random Sampling
    • Core Set + Greedy Selection
  • 🌊 Stream-based Active Learning: Classification
    • Split
    • Periodic Sampling
    • Stream Random Sampling
    • Variable-Uncertainty
    • Density Based Active Learning for Data Streams
    • Fixed-Uncertainty
    • Randomized-Variable-Uncertainty
    • Cognitive Dual-Query Strategy with Random Sampling
    • Cognitive Dual-Query Strategy with Variable-Uncertainty
    • Cognitive Dual-Query Strategy with Randomized-Variable-Uncertainty
    • Cognitive Dual-Query Strategy with Fixed-Uncertainty
    • Probabilistic Active Learning in Datastreams
  • Visualizations
  • 🏊 Pool-based Active Learning: Multi-annotator Learning

🏊 Pool-based Active Learning: Multi-annotator Learning#

../../../_images/pool_multi_annotator_legend.png

Interval Estimation Threshold

Interval Estimation Threshold

Random Sampling

Random Sampling

Core Set + Greedy Selection

Core Set + Greedy Selection

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Regression Tree Based Active Learning (RT-AL) with Representativity Selection

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Interval Estimation Threshold
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