Age Estimation is the task of estimating the age of a person from an image.
( Image credit: BridgeNet )
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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.
Ranked #1 on Age Estimation on AFAD
The proposed method employs a multi-task learning framework that regularizes the shared parameters of CNN and builds a synergy among different domains and tasks.
Ranked #9 on Face Verification on IJB-A
Recently, MobileNets and ShuffleNets have been proposed to reduce the number of parameters, yielding lightweight models.
Ranked #1 on Age Estimation on FGNET
Residual representation learning simplifies the optimization problem of learning complex functions and has been widely used by traditional convolutional neural networks.
Ranked #1 on Age Estimation on CACD
However, it is difficult to collect sufficient training images with precise labels in some domains such as apparent age estimation, head pose estimation, multi-label classification and semantic segmentation.
Ranked #1 on Age Estimation on ChaLearn 2015
We propose order learning to determine the order graph of classes, representing ranks or priorities, and classify an object instance into one of the classes.
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.
By discretizing the target into a set of non-overlapping classes, it has been shown that training a classifier can improve neural network accuracy compared to using a standard regression approach.
Ranked #1 on Age Estimation on UTKFace