Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

Recently, due to the availability of big data and the rapid growth of computing power,
artificial intelligence (AI) has regained tremendous attention and investment. Machine …

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

Transfer learning has recently become the dominant paradigm of machine learning. Pre-
trained models fine-tuned for downstream tasks achieve better performance with fewer …

In natural images, information is conveyed at different frequencies where higher frequencies
are usually encoded with fine details and lower frequencies are usually encoded with global …

Inspired by the human cognitive system, attention is a mechanism that imitates the human
cognitive awareness about specific information, amplifying critical details to focus more on …

This paper presents a method for adding multiple tasks to a single deep neural network
while avoiding catastrophic forgetting. Inspired by network pruning techniques, we exploit …

Large neural networks excel in many domains, but they are expensive to train and fine-tune.
A popular approach to reduce their compute or memory requirements is to replace dense …

Deep neural networks enable state-of-the-art accuracy on visual recognition tasks such as
image classification and object detection. However, modern deep networks contain millions …