SOTAVerified

General Classification

Algorithms trying to solve the general task of classification.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8976–9000 of 14581 papers

TitleStatusHype
DA-LD-Hildesheim at SemEval-2019 Task 6: Tracking Offensive Content with Deep Learning using Shallow Representation—0
Dance Hit Song Prediction—0
DART: Distribution Aware Retinal Transform for Event-based Cameras—0
DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification—0
Dartmouth CS at WNUT-2020 Task 2: Informative COVID-19 Tweet Classification Using BERT—0
Data-adaptive Active Sampling for Efficient Graph-Cognizant Classification—0
Data Analysis of Wireless Networks Using Classification Techniques—0
Data Augmentation and Clustering for Vehicle Make/Model Classification—0
Data augmentation and feature selection for automatic model recommendation in computational physics—0
Data augmentation approaches for improving animal audio classification—0
Data Augmentation Based on Distributed Expressions in Text Classification Tasks—0
Data Augmentation by Pairing Samples for Images Classification—0
Data Augmentation for Deep Learning-based Radio Modulation Classification—0
E-Stitchup: Data Augmentation for Pre-Trained Embeddings—0
Data Augmentation for Electrocardiogram Classification with Deep Neural Network—0
Data Augmentation for Skin Lesion using Self-Attention based Progressive Generative Adversarial Network—0
Data Augmentation for Visual Question Answering—0
Data Augmentation Imbalance For Imbalanced Attribute Classification—0
Data Augmentation in Emotion Classification Using Generative Adversarial Networks—0
Data Augmentation of IMU Signals and Evaluation via a Semi-Supervised Classification of Driving Behavior—0
Data Augmentation Using Adversarial Training for Construction-Equipment Classification—0
Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery—0
Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness—0
Data Centroid Based Multi-Level Fuzzy Min-Max Neural Network—0
Data classification using the Dempster-Shafer method—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
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