Age Estimation
51 papers with code • 11 benchmarks • 15 datasets
Age Estimation is the task of estimating the age of a person from an image some other kind of data.
( Image credit: BridgeNet )
Benchmarks
These leaderboards are used to track progress in Age Estimation
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UTKFace
MORPH
CACD
FG-NET
AADB
MegaAge
PhysioNet Challenge 2021
KANFace
Most implemented papers
Rank consistent ordinal regression for neural networks with application to age estimation
Raschka-research-group/coral-cnn
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In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category cross-entropy.
Face Aging With Conditional Generative Adversarial Networks
Vishal-V/GSoC-TensorFlow-2019
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It has been recently shown that Generative Adversarial Networks (GANs) can produce synthetic images of exceptional visual fidelity.
Deep Regression Forests for Age Estimation
Age estimation from facial images is typically cast as a nonlinear regression problem.
On the effect of age perception biases for real age regression
Our model outperformed a state-of-the-art architecture proposed to separately address apparent and real age regression.
Facial age estimation by deep residual decision making
Nicholasli1995/VisualizingNDF
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Residual representation learning simplifies the optimization problem of learning complex functions and has been widely used by traditional convolutional neural networks.
Fair and accurate age prediction using distribution aware data curation and augmentation
davidberend/FairnessMatters
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One of the main causes of unfair behavior in age prediction methods lies in the distribution and diversity of the training data.
PFA-GAN: Progressive Face Aging with Generative Adversarial Network
Hzzone/PFA-GAN
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Although impressive results have been achieved with conditional generative adversarial networks (cGANs), the existing cGANs-based methods typically use a single network to learn various aging effects between any two different age groups.
Improved Brain Age Estimation with Slice-based Set Networks
umgupta/2d-slice-set-networks-for-brain-age
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Deep Learning for neuroimaging data is a promising but challenging direction.
OTFPF: Optimal Transport-Based Feature Pyramid Fusion Network for Brain Age Estimation with 3D Overlapped ConvNeXt
In this paper, we propose an end-to-end neural network architecture, referred to as optimal transport based feature pyramid fusion (OTFPF) network, for the brain age estimation with T1 MRIs.
Robust Optimization for Deep Regression
bazilas/matconvnet-deepReg
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ICCV 2015
Convolutional Neural Networks (ConvNets) have successfully contributed to improve the accuracy of regression-based methods for computer vision tasks such as human pose estimation, landmark localization, and object detection.
