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K
الرئيسية
SOTA
Learning With Noisy Labels
Learning With Noisy Labels On Animal
Learning With Noisy Labels On Animal
المقاييس
Accuracy
ImageNet Pretrained
Network
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
Columns
اسم النموذج
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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