← Learning with noisy labels

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ANIMAL

아카이브 평가 19건 · 마지막 적재/변경 2026-10-30

원본 데이터셋 링크 1

평가 기록

과거 수집 기록이며 최신 순위가 아닙니다. 데이터셋 버전·split·실험 조건과 지표 방향이 검증되지 않은 결과는 직접 비교할 수 없습니다. 날짜는 논문 날짜이며 최신 평가일을 뜻하지 않습니다.

표가 화면보다 넓으면 좌우로 스크롤하여 논문과 구현 링크를 확인하세요.

원본 기록 순서 · 순위 미부여
모델	원본 지표·값	논문	구현
Jigsaw-ViT	Accuracy 89.0 Network DeiT-S ImageNet Pretrained NO	Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer 2022-07-25	코드 1
SURE	Accuracy 89.0 Network Vgg19-BN ImageNet Pretrained NO	SURE: SUrvey REcipes for building reliable and robust deep networks 2024-03-01	코드 1
CLIPCleaner	Accuracy 88.85 Network Vgg19-BN ImageNet Pretrained NO	CLIPCleaner: Cleaning Noisy Labels with CLIP 2024-08-19	코드 1
PSSCL	Accuracy 88.74 Network Vgg19-BN ImageNet Pretrained NO	PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels 2024-12-18	코드 1
SSR	Accuracy 88.5 Network Vgg19-BN ImageNet Pretrained NO	SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise 2021-11-22	코드 1
BtR	Accuracy 88.5 Network Vgg19-BN ImageNet Pretrained NO	Bootstrapping the Relationship Between Images and Their Clean and Noisy Labels 2022-10-17	코드 1
SPR	Accuracy 86.8 Network VGG19-BN ImageNet Pretrained NO	Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels 2022-03-15	코드 1
Dynamic Loss	Accuracy 86.5 Network Vgg19-BN ImageNet Pretrained NO	Dynamic Loss For Robust Learning 2022-11-22	코드 1
GNL	Accuracy 85.9 Network Vgg-19-BN ImageNet Pretrained NO	Partial Label Supervision for Agnostic Generative Noisy Label Learning 2023-08-02	코드 1
C2MT	Accuracy 85.9 Network Vgg-19-BN ImageNet Pretrained NO	Cross-to-merge training with class balance strategy for learning with noisy labels 2024-04-01	코드 1
InstanceGM with ConvNeXt	Accuracy 84.7 Network ConvNeXt ImageNet Pretrained NO	Instance-Dependent Noisy Label Learning via Graphical Modelling 2022-09-02	코드 1
InstanceGM	Accuracy 84.6 Network Vgg19-BN ImageNet Pretrained NO	Instance-Dependent Noisy Label Learning via Graphical Modelling 2022-09-02	코드 1
Nested+Co-teaching (NCT)	Accuracy 84.1 Network Vgg19-BN ImageNet Pretrained NO	Compressing Features for Learning with Noisy Labels 2022-06-27	코드 1
PLC	Accuracy 83.4 Network Vgg19-BN ImageNet Pretrained NO	Learning with Feature-Dependent Label Noise: A Progressive Approach 2021-03-13	코드 1
InstanceGM with ResNet	Accuracy 82.3 Network ResNet ImageNet Pretrained NO	Instance-Dependent Noisy Label Learning via Graphical Modelling 2022-09-02	코드 1
SELFIE	Accuracy 81.8 Network Vgg19-BN ImageNet Pretrained NO	SELFIE: Refurbishing Unclean Samples for Robust Deep Learning 2019-06-15	코드 1
CE + Dropout	Accuracy 81.3 Network Vgg19-BN ImageNet Pretrained NO	Boosting Co-teaching with Compression Regularization for Label Noise 2021-04-28	코드 1
Nested Dropout	Accuracy 81.3 Network Vgg19-BN ImageNet Pretrained NO	Boosting Co-teaching with Compression Regularization for Label Noise 2021-04-28	코드 1
Cross Entropy	Accuracy 79.4 Network Vgg19-BN ImageNet Pretrained NO	Learning with Feature-Dependent Label Noise: A Progressive Approach 2021-03-13	코드 1