Multi-class Classification
234 papers with code • 4 benchmarks • 11 datasets
Benchmarks
These leaderboards are used to track progress in Multi-class Classification
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Libraries
Use these libraries to find Multi-class Classification models and implementationsDatasets
SMM4H
CVE
SF-MASK
DeepParliament
TII-SSRC-23
Most implemented papers
sEMG Gesture Recognition with a Simple Model of Attention
Myoelectric control is one of the leading areas of research in the field of robotic prosthetics.
Deep brain state classification of MEG data
SMehrkanoon/Deep-brain-state-classification-of-MEG-data
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The experimental results of cross subject multi-class classification on the studied MEG dataset show that the inclusion of attention improves the generalization of the models across subjects.
Efficient Robust Optimal Transport with Application to Multi-Label Classification
Optimal transport (OT) is a powerful geometric tool for comparing two distributions and has been employed in various machine learning applications.
IoTDevID: A Behavior-Based Device Identification Method for the IoT
Device identification is one way to secure a network of IoT devices, whereby devices identified as suspicious can subsequently be isolated from a network.
Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions
Generalised linear models for multi-class classification problems are one of the fundamental building blocks of modern machine learning tasks.
VISTA: Vision Transformer enhanced by U-Net and Image Colorfulness Frame Filtration for Automatic Retail Checkout
guglielmocamporese/hands-segmentation-pytorch
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Multi-class product counting and recognition identifies product items from images or videos for automated retail checkout.
Generalized Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses
Shuijing2018/CwR_Mindspore
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Conference 2022
\emph{Classification with rejection} (CwR) refrains from making a prediction to avoid critical misclassification when encountering test samples that are difficult to classify.
1st Place Solution for PSG competition with ECCV'22 SenseHuman Workshop
wangqixun/mfpsg
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Panoptic Scene Graph (PSG) generation aims to generate scene graph representations based on panoptic segmentation instead of rigid bounding boxes.
Neuro-symbolic Rule Learning in Real-world Classification Tasks
Neuro-symbolic rule learning has attracted lots of attention as it offers better interpretability than pure neural models and scales better than symbolic rule learning.
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning
Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data.
