SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Showing 29612970 of 3073 papers

TitleStatusHype
FisherMask: Enhancing Neural Network Labeling Efficiency in Image Classification Using Fisher InformationCode0
OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image ClassificationCode0
OpenAL: Evaluation and Interpretation of Active Learning StrategiesCode0
To Softmax, or not to Softmax: that is the question when applying Active Learning for Transformer ModelsCode0
Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings (Extended Version)Code0
FOCAL: A Cost-Aware Video Dataset for Active LearningCode0
SoQal: Selective Oracle Questioning for Consistency Based Active Learning of Cardiac SignalsCode0
Active Learning for Graph EmbeddingCode0
Automatic Segmentation of the Spinal Cord Nerve RootletsCode0
FOIT: Fast Online Instance Transfer for Improved EEG Emotion RecognitionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified