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SOTA
노이즈 있는 라벨로 학습
Learning With Noisy Labels On Animal
Learning With Noisy Labels On Animal
평가 지표
Accuracy
ImageNet Pretrained
Network
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
Accuracy
ImageNet Pretrained
Network
Paper Title
Repository
Jigsaw-ViT
89.0
NO
DeiT-S
Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer
-
Cross Entropy
79.4
NO
Vgg19-BN
Learning with Feature-Dependent Label Noise: A Progressive Approach
-
Nested Dropout
81.3
NO
Vgg19-BN
Boosting Co-teaching with Compression Regularization for Label Noise
-
PLC
83.4
NO
Vgg19-BN
Learning with Feature-Dependent Label Noise: A Progressive Approach
-
C2MT
85.9
NO
Vgg-19-BN
Cross-to-merge training with class balance strategy for learning with noisy labels
CE + Dropout
81.3
NO
Vgg19-BN
Boosting Co-teaching with Compression Regularization for Label Noise
-
SURE
89.0
NO
Vgg19-BN
SURE: SUrvey REcipes for building reliable and robust deep networks
-
SELFIE
81.8
NO
Vgg19-BN
SELFIE: Refurbishing Unclean Samples for Robust Deep Learning
PSSCL
88.74
NO
Vgg19-BN
PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels
Dynamic Loss
86.5
NO
Vgg19-BN
Dynamic Loss For Robust Learning
-
InstanceGM
84.6
NO
Vgg19-BN
Instance-Dependent Noisy Label Learning via Graphical Modelling
-
SSR
88.5
NO
Vgg19-BN
SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise
-
InstanceGM with ConvNeXt
84.7
NO
ConvNeXt
Instance-Dependent Noisy Label Learning via Graphical Modelling
-
Nested+Co-teaching (NCT)
84.1
NO
Vgg19-BN
Compressing Features for Learning with Noisy Labels
-
BtR
88.5
NO
Vgg19-BN
Bootstrapping the Relationship Between Images and Their Clean and Noisy Labels
-
SPR
86.8
NO
VGG19-BN
Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels
-
GNL
85.9
NO
Vgg-19-BN
Partial Label Supervision for Agnostic Generative Noisy Label Learning
-
InstanceGM with ResNet
82.3
NO
ResNet
Instance-Dependent Noisy Label Learning via Graphical Modelling
-
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