A number of computer vision deep regression approaches report improved results when
adding a classification loss to the regression loss. Here, we explore why this is useful in …

In computer vision, it is often observed that formulating regression problems as a
classification task often yields better performance. We investigate this curious phenomenon …

Deep regression models typically learn in an end-to-end fashion without explicitly
emphasizing a regression-aware representation. Consequently, the learned representations …

Data imbalance is easily found in annotated data when the observations of certain
continuous label values are difficult to collect for regression tasks. When they come to …

Existing regression models tend to fall short in both accuracy and uncertainty estimation
when the label distribution is imbalanced. In this paper, we propose a probabilistic deep …

Predictive Autoscaling is used to forecast the workloads of servers and prepare the
resources in advance to ensure service level objectives (SLOs) in dynamic cloud …

Imbalanced distributions are ubiquitous in real-world data. They create constraints on Deep
Neural Networks to represent the minority labels and avoid bias towards majority labels. The …

Rapid urbanization has significantly escalated traffic congestion, underscoring the need for
advanced congestion prediction services to bolster intelligent transportation systems. As one …

Regression tasks in computer vision such as age estimation or counting are often formulated
into classification by quantizing the target space into classes. Yet real-world data is often …

Deep neural networks have shown promise in predicting the chronological age of a healthy
brain using T1-weighted magnetic resonance images (T1 MRIs). This predicted brain age …