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 20612070 of 3073 papers

TitleStatusHype
Towards Human-Machine Cooperation: Self-supervised Sample Mining for Object Detection0
Towards more Reliable Transfer Learning0
Towards ontology driven learning of visual concept detectors0
Towards Overcoming Practical Obstacles to Deploying Deep Active Learning0
Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection0
Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection0
Representative Subset Selection for Efficient Fine-Tuning in Self-Supervised Speech Recognition0
Towards Unconstrained 2D Pose Estimation of the Human Spine0
Toward Supervised Anomaly Detection0
Towards Visual Explainable Active Learning for Zero-Shot Classification0
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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