Deep learning applies multiple processing layers to learn representations of data with
multiple levels of feature extraction. This emerging technique has reshaped the research …

Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

In this paper, we introduce a new large-scale face dataset named VGGFace2. The dataset
contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each …

Deep domain adaptation has emerged as a new learning technique to address the lack of
massive amounts of labeled data. Compared to conventional methods, which learn shared …

Convolutional neural network-based approaches for semantic segmentation rely on
supervision with pixel-level ground truth, but may not generalize well to unseen image …

The volume of convolutional neural network (CNN) models proposed for face recognition
has been continuously growing larger to better fit the large amount of training data. When …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

Deep learning, in particular the deep convolutional neural networks, has received
increasing interests in face recognition recently, and a number of deep learning methods …

Deep models trained on source domain lack generalization when evaluated on unseen
target domains with different data distributions. The problem becomes even more …

In unsupervised domain adaptation, rich domain-specific characteristics bring great
challenge to learn domain-invariant representations. However, domain discrepancy is …