SOTAVerified

General Classification

Algorithms trying to solve the general task of classification.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9376–9400 of 14581 papers

TitleStatusHype
Deep vs. Diverse Architectures for Classification Problems—0
DeepWaste: Applying Deep Learning to Waste Classification for a Sustainable Planet—0
Deep Weisfeiler-Lehman Assignment Kernels via Multiple Kernel Learning—0
Deep Within-Class Covariance Analysis for Robust Audio Representation Learning—0
DeepWiTraffic: Low Cost WiFi-Based Traffic Monitoring System Using Deep Learning—0
Defending Adversarial Attacks via Semantic Feature Manipulation—0
Defending Against Multiple and Unforeseen Adversarial Videos—0
Defending Malware Classification Networks Against Adversarial Perturbations with Non-Negative Weight Restrictions—0
Defensive Player Classification in the National Basketball Association—0
Deformable Gabor Feature Networks for Biomedical Image Classification—0
DEFRAG: Deep Euclidean Feature Representations through Adaptation on the Grassmann Manifold—0
DEFT 2018: Attention s\'elective pour classification de microblogs (DEFT 2018 : Selective Attention for Microblogging Classification )—0
Degree based Classification of Harmful Speech using Twitter Data—0
Degrees of Freedom in Deep Neural Networks—0
Delaunay Triangulation on Skeleton of Flowers for Classification—0
Delay Learning Architectures for Memory and Classification—0
Delegating Custom Object Detection Tasks to a Universal Classification System—0
Delineation of Skin Strata in Reflectance Confocal Microscopy Images using Recurrent Convolutional Networks with Toeplitz Attention—0
Dementia Severity Classification under Small Sample Size and Weak Supervision in Thick Slice MRI—0
DeMIAN: Deep Modality Invariant Adversarial Network—0
Demodulation of Sparse PPM Signals with Low Samples Using Trained RIP Matrix—0
Demographic Factors Improve Classification Performance—0
Demographic Word Embeddings for Racism Detection on Twitter—0
Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases—0
Demystifying Loss Functions for Classification—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