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الرئيسية
SOTA
التعلم مع العلامات الضوضائية
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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