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 16211630 of 3073 papers

TitleStatusHype
Towards Efficient Active Learning in NLP via Pretrained Representations0
Towards Efficient Disaster Response via Cost-effective Unbiased Class Rate Estimation through Neyman Allocation Stratified Sampling Active Learning0
Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Sets0
Towards Fewer Labels: Support Pair Active Learning for Person Re-identification0
Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder0
Towards Global-Scale Crowd+AI Techniques to Map and Assess Sidewalks for People with Disabilities0
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
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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