SOTAVerified

General Classification

Algorithms trying to solve the general task of classification.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6176–6200 of 14581 papers

TitleStatusHype
Unsupervised Paraphasia Classification in Aphasic Speech—0
Unsupervised part learning for visual recognition—0
Unsupervised Part Mining for Fine-grained Image Classification—0
Unsupervised patient representations from clinical notes with interpretable classification decisions—0
Unsupervised Person Re-identification via Multi-label Classification—0
Unsupervised Place Discovery for Visual Place Classification—0
Unsupervised Pre-trained Models from Healthy ADLs Improve Parkinson's Disease Classification of Gait Patterns—0
Unsupervised Regenerative Learning of Hierarchical Features in Spiking Deep Networks for Object Recognition—0
Self-Supervised Learning of Context-Aware Pitch Prosody Representations—0
Unsupervised Representation Learning of DNA Sequences—0
Unsupervised Segmentation Algorithms' Implementation in ITK for Tissue Classification via Human Head MRI Scans—0
Unsupervised Segmentation of Multispectral Images with Cellular Automata—0
Unsupervised seismic facies classification using deep convolutional autoencoder—0
Unsupervised Sequence Classification using Sequential Output Statistics—0
Unsupervised shape and motion analysis of 3822 cardiac 4D MRIs of UK Biobank—0
Unsupervised single-particle deep clustering via statistical manifold learning—0
Unsupervised Star Galaxy Classification with Cascade Variational Auto-Encoder—0
Unsupervised Steganalysis Based on Artificial Training Sets—0
Unsupervised Structure Discovery for Semantic Analysis of Audio—0
Unsupervised Task Design to Meta-Train Medical Image Classifiers—0
Unsupervised vehicle recognition using incremental reseeding of acoustic signatures—0
Unsupervised Visual Feature Learning with Spike-timing-dependent Plasticity: How Far are we from Traditional Feature Learning Approaches?—0
Untangling AdaBoost-based Cost-Sensitive Classification. Part I: Theoretical Perspective—0
Untangling AdaBoost-based Cost-Sensitive Classification. Part II: Empirical Analysis—0
Untangling Local and Global Deformations in Deep Convolutional Networks for Image Classification and Sliding Window Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1KNNAccuracy93.1—Unverified
2RFAccuracy92.96—Unverified
3CARTAccuracy92.82—Unverified
4LDAAccuracy77.22—Unverified
#ModelMetricClaimedVerifiedStatus
1MONT3Accuracy100—Unverified
2XBNETAccuracy100—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy42—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy67.7—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy68.5—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy13.4—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy90.6—Unverified
#ModelMetricClaimedVerifiedStatus
1MONT3Accuracy100—Unverified