PML: Progressive Margin Loss for Long-tailed Age Classification

Understand

In this paper, we propose a progressive margin loss (PML) approach for unconstrained facial age classification.

• Conventional methods make strong assumption on that each class owns adequate instances to outline its data distribution, likely leading to bias prediction where the training samples are sparse across age classes.
• Instead, our PML aims to adaptively refine the age label pattern by enforcing a couple of margins, which fully takes in the in-between discrepancy of the intra-class variance, inter-class variance and class center.
• Our PML typically incorporates with the ordinal margin and the variational margin, simultaneously plugging in the globally-tuned deep neural network paradigm.