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Classification

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하위 태스크: 24
평가표: 72
보존된 평가 기록: 134

Classification is the task of categorizing a set of data into predefined classes or groups. The aim of classification is to train a model to correctly predict the class or group of new, unseen data. The model is trained on a labeled dataset where each instance is assigned a class label. The learning algorithm then builds a mapping between the features of the data and the class labels. This mapping is then used to predict the class label of new, unseen data points. The quality of the prediction is usually evaluated using metrics such as accuracy, precision, and recall.

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Classification

논문

Robust performance metrics for imbalanced classification problems

Task-Specific Audio Coding for Machines: Machine-Learned Latent Features Are Codes for That Machine

Making Language Model a Hierarchical Classifier and Generator

MUPAX: Multidimensional Problem Agnostic eXplainable AI

Adversarial attacks to image classification systems using evolutionary algorithms

NonverbalTTS: A Public English Corpus of Text-Aligned Nonverbal Vocalizations with Emotion Annotations for Text-to-Speech

Efficient Calisthenics Skills Classification through Foreground Instance Selection and Depth Estimation

Safeguarding Federated Learning-based Road Condition Classification

Tomato Multi-Angle Multi-Pose Dataset for Fine-Grained Phenotyping

AI-Enhanced Pediatric Pneumonia Detection: A CNN-Based Approach Using Data Augmentation and Generative Adversarial Networks (GANs)

FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation

Fuzzy Classification Aggregation for a Continuum of Agents

Hybrid-View Attention for csPCa Classification in TRUS

Robustness of Misinformation Classification Systems to Adversarial Examples Through BeamAttack

The Trilemma of Truth in Large Language Models

DAPFAM: A Domain-Aware Patent Retrieval Dataset Aggregated at the Family Level

A Semi-supervised Scalable Unified Framework for E-commerce Query Classification

Devising a solution to the problems of Cancer awareness in Telangana

Perspectives in Play: A Multi-Perspective Approach for More Inclusive NLP Systems

Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models

Disentangled representations of microscopy images

The receptron is a nonlinear threshold logic gate with intrinsic multi-dimensional selective capabilities for analog inputs

One Prototype Is Enough: Single-Prototype Activation for Interpretable Image Classification

Revisiting R: Statistical Envelope Analysis for Lightweight RF Modulation Classification

Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons

Fully Few-shot Class-incremental Audio Classification Using Multi-level Embedding Extractor and Ridge Regression Classifier

SIM-Net: A Multimodal Fusion Network Using Inferred 3D Object Shape Point Clouds from RGB Images for 2D Classification

Can Generated Images Serve as a Viable Modality for Text-Centric Multimodal Learning?

Brain-inspired interpretable reservoir computing with resonant recurrent neural networks

Robust Training with Data Augmentation for Medical Imaging Classification

Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy

Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation

SHREC and PHEONA: Using Large Language Models to Advance Next-Generation Computational Phenotyping

FedWSIDD: Federated Whole Slide Image Classification via Dataset Distillation

Acoustic scattering AI for non-invasive object classifications: A case study on hair assessment