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 11911200 of 3073 papers

TitleStatusHype
Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge0
ImitAL: Learned Active Learning Strategy on Synthetic DataCode0
Lirot.ai: A Novel Platform for Crowd-Sourcing Retinal Image Segmentations0
Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Training0
Open Long-Tailed Recognition in a Dynamic World0
Semi-supervised Learning with Deterministic Labeling and Large Margin ProjectionCode0
Generating a Terrain-Robustness Benchmark for Legged Locomotion: A Prototype via Terrain Authoring and Active Learning0
Bucketized Active Sampling for Learning ACOPF0
Continuous Active Learning Using Pretrained Transformers0
BenchPress: A Deep Active Benchmark GeneratorCode1
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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