Face attribute classification with evidential deep learning

Official URL: http://dx.doi.org/10.1117/12.2680634

We address the problem of uncertainty quantification in the domain of face attribute classification, using Evidential Deep Learning (EDL) framework. The proposed EDL approach leverages the strength of Convolution Neural Networks (CNN), with the objective of representing the uncertainty in the output predictions. Predominantly, the softmax/sigmoid activation functions are applied to map the output logits of the CNN to target class probabilities in multi-class classification problems. By replacing the standard softmax/sigmoid output of a CNN with the parameters of the evidential distribution, EDL learns to represent the uncertainty in its predictions. The proposed approach is evaluated on CelebA and LFWA datasets. The quantitative and qualitative analysis demonstrate the suitability and strength of EDL to estimate the uncer- tainty in the output predictions without hindering the accuracy of CNN-based models.