SOTAVerified

General Classification

Algorithms trying to solve the general task of classification.

Papers

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Showing 8751–8775 of 14581 papers

TitleStatusHype
Continual Learning with Extended Kronecker-factored Approximate Curvature—0
Continual Local Replacement for Few-shot Learning—0
Continuous and Simultaneous Gesture and Posture Recognition for Commanding a Robotic Wheelchair; Towards Spotting the Signal Patterns—0
Continuous Convolutional Neural Networks for Image Classification—0
Continuous Learning for Large-scale Personalized Domain Classification—0
Continuous N-gram Representations for Authorship Attribution—0
Continuous Speech Recognition using EEG and Video—0
Continuum directions for supervised dimension reduction—0
ContourCNN: convolutional neural network for contour data classification—0
Contour Detection and Characterization for Asynchronous Event Sensors—0
Contour location via entropy reduction leveraging multiple information sources—0
Contour location via entropy reduction leveraging multiple information sources—0
Contraction Mapping of Feature Norms for Classifier Learning on the Data with Different Quality—0
Contractive Rectifier Networks for Nonlinear Maximum Margin Classification—0
Contrast Adaptive Tissue Classification by Alternating Segmentation and Synthesis—0
Contrastive estimation reveals topic posterior information to linear models—0
Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification—0
Contrastive Learning of Single-Cell Phenotypic Representations for Treatment Classification—0
Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections—0
Contrastive Self-supervised Learning for Graph Classification—0
Contrastive Self-Supervised Learning of Global-Local Audio-Visual Representations—0
Contrast Phase Classification with a Generative Adversarial Network—0
Controllable Invariance through Adversarial Feature Learning—0
Controlling for Confounders in Multimodal Emotion Classification via Adversarial Learning—0
Controlling for Unobserved Confounds in Classification Using Correlational Constraints—0
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All datasetsXORirisActivityFashion-MNISTISOLETMice ProteinMNISTWine

Benchmark Results

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