PK PyTorchKR Papers
Papers with Code · CC BY-SA 4.0

Classification

1,821 평가 기록 397 datasets

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.

개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.

벤치마크

Papers with Code 아카이브의 데이터셋별 기록 예시입니다. 평가 조건과 지표 방향이 검증되지 않아 순위를 의미하지 않습니다.

Dataset기록된 모델 예시MetricPaperCode
HGP-SL
Accuracy: 84.91
논문 코드
Evolution of Graph Classifiers
Accuracy: 100.00%Accuracy (10-fold): 100
논문 코드
TFGW ADJ (L=2)
Accuracy: 88.1%
논문 코드
ESA (Edge set attention, no positional encodings)
Accuracy: 79.423±1.658
논문 코드
D&D
U2GNN (Unsupervised)
Accuracy: 95.67%
논문 코드
OmniVec2
Test mAP: 0.558
논문 -
U2GNN (Unsupervised)
Accuracy: 96.41%
논문 코드
ESA + RWSE (Edge set attention, Random Walk Structural Encoding, + validation set)
AP: 0.7479
논문 코드
U2GNN (Unsupervised)
Accuracy: 95.62%
논문 코드
WKPI-kcenters
Accuracy: 87.3
논문 코드
PTC
U2GNN (Unsupervised)
Accuracy: 91.81%
논문 코드
U2GNN (Unsupervised)
Accuracy: 89.2%
논문 코드

데이터셋

397개
PROTEINSMUTAGNCI1ENZYMESD&DAudioSetIMDb-BPeptides-funcCOLLABNCI109PTCIMDb-M

논문

라이브러리

Papers with Code 리더보드 row에 연결된 구현 저장소입니다.

github.com/davide-coccomini/mintime-multi-identity-size-invariant-timesformer-for-video-deepfake-detection 1 논문
github.com/ZidongLiu/DeComFL 1 논문
github.com/ADCLab/RedditIdeologyDB 1 논문
github.com/amirrezafateh/multi-scale-transformer-pneumonia 1 논문
github.com/KatherLab/STAMP 1 논문
github.com/pranavsinghps1/CASS 1 논문
github.com/sweetalyssum/Seq2Seq-DU 1 논문
github.com/weathon/Lang-Gas 1 논문

하위 태스크

GC Graph Classification 76 데이터셋 · 844 rows TC Text Classification 191 데이터셋 · 583 rows AC Audio Classification 29 데이터셋 · 204 rows MI Medical Image Classification 12 데이터셋 · 24 rows LW Learning with coarse labels 4 데이터셋 · 8 rows MC Multi-modal Classification 3 데이터셋 · 8 rows MC Multi-class Classification 5 데이터셋 · 5 rows MC Mitigating Contextual Bias 1 데이터셋 · 4 rows HI Hyperspectral Image-Based Fruit Ripeness Prediction 1 데이터셋 · 3 rows CS Clinical Section Identification 1 데이터셋 · 2 rows CC Classifier calibration 1 데이터셋 · 1 rows SC Sensitivity Classification 1 데이터셋 · 1 rows