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SOTA
Age Estimation
Age Estimation On Chalearn 2015
Age Estimation On Chalearn 2015
Metrics
e-error
Results
Performance results of various models on this benchmark
Columns
Model Name
e-error
Paper Title
DLDL+VGG-Face
0.31
Deep Label Distribution Learning with Label Ambiguity
DLDL-v2 (ThinAgeNet)
0.272
Learning Expectation of Label Distribution for Facial Age and Attractiveness Estimation
AgeNet
0.270685
AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation
DEX
0.264975
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks
BridgeNet
0.255140
BridgeNet: A Continuity-Aware Probabilistic Network for Age Estimation
MetaAge
0.250651
MetaAge: Meta-Learning Personalized Age Estimators
MWR
-
Moving Window Regression: A Novel Approach to Ordinal Regression
0 of 7 row(s) selected.
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Next
HyperAI
HyperAI
Home
Console
Docs
News
Papers
Tutorials
Datasets
Wiki
SOTA
LLM Models
GPU Leaderboard
Events
Search
About
Terms of Service
Privacy Policy
English
HyperAI
HyperAI
Toggle Sidebar
Search the site…
⌘
K
Command Palette
Search for a command to run...
Console
Home
SOTA
Age Estimation
Age Estimation On Chalearn 2015
Age Estimation On Chalearn 2015
Metrics
e-error
Results
Performance results of various models on this benchmark
Columns
Model Name
e-error
Paper Title
DLDL+VGG-Face
0.31
Deep Label Distribution Learning with Label Ambiguity
DLDL-v2 (ThinAgeNet)
0.272
Learning Expectation of Label Distribution for Facial Age and Attractiveness Estimation
AgeNet
0.270685
AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation
DEX
0.264975
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks
BridgeNet
0.255140
BridgeNet: A Continuity-Aware Probabilistic Network for Age Estimation
MetaAge
0.250651
MetaAge: Meta-Learning Personalized Age Estimators
MWR
-
Moving Window Regression: A Novel Approach to Ordinal Regression
0 of 7 row(s) selected.
Previous
Next