1 research outputs found
Directing DNNs Attention for Facial Attribution Classification using Gradient-weighted Class Activation Mapping
Deep neural networks (DNNs) have a high accuracy on image classification
tasks. However, DNNs trained by such dataset with co-occurrence bias may rely
on wrong features while making decisions for classification. It will greatly
affect the transferability of pre-trained DNNs. In this paper, we propose an
interactive method to direct classifiers paying attentions to the regions that
are manually specified by the users, in order to mitigate the influence of
co-occurrence bias. We test on CelebA dataset, the pre-trained AlexNet is
fine-tuned to focus on the specific facial attributes based on the results of
Grad-CAM.Comment: CVPR-19 Workshop on Explainable A