Deep neural networks are at the forefront of machine learning research. However, despite
achieving impressive performance on complex tasks, they can be very sensitive: Small …

Multi-task learning (MTL) is a subfield of machine learning in which multiple tasks are
simultaneously learned by a shared model. Such approaches offer advantages like …

With the advent of deep learning, many dense prediction tasks, ie, tasks that produce pixel-
level predictions, have seen significant performance improvements. The typical approach is …

The vast majority of deep models use multiple gradient signals, typically corresponding to a
sum of multiple loss terms, to update a shared set of trainable weights. However, these …

In this paper, we argue about the importance of considering task interactions at multiple
scales when distilling task information in a multi-task learning setup. In contrast to common …

In this work we address task interference in universal networks by considering that a network
is trained on multiple tasks, but performs one task at a time, an approach we refer to as" …

Convolutional neural networks (CNNs) have shown outstanding image classification
performance, having been successfully applied in several real‐world applications. However …

Advances in machine learning have led to broad deployment of systems with impressive
performance on important problems. Nonetheless, these systems can be induced to make …

Whether what you see in Figure 1 is a" flamingo" or a" bird", is the question we ask in this
paper. While fine-grained visual classification (FGVC) strives to arrive at the former, for the …

Multi-task networks are commonly utilized to alleviate the need for a large number of highly
specialized single-task networks. However, two common challenges in developing multi …