Convolutional neural networks (CNNs) have shown excellent performance in numerous
computer vision tasks. However, the high computational and memory demands in computer …

The first-order optimizers in deep neural networks (DNN) are of pivotal essence for a
concrete loss function to reach the local minimum or global one on the loss surface within …

We present a filter correlation based model compression approach for deep convolutional
neural networks. Our approach iteratively identifies pairs of filters with the largest pairwise …

Paddy is an essential nutrient worldwide. Rice gives 21% of worldwide human per capita
energy and 15% of per capita protein. Asia represented 60% of the worldwide populace …

With the increase of structure complexity, convolutional neural networks (CNNs) take a fair
amount of computation cost. Meanwhile, existing research reveals the salient parameter …

Network pruning and knowledge distillation, as two effective network compression
techniques, have drawn extensive attention due to their success in reducing model …

Beyond the complexity of CNNs that require training on large annotated datasets, the
domain shift between design and operational data has limited the adoption of CNNs in many …

In the last decade, pattern recognition and decision making from images has mainly focused
on the development of deep learning architectures, with different types of networks such as …

Structured pruning is a well-established technique for compressing neural networks, making
them suitable for deployment in resource-limited edge devices. This study presents an …

With the rapid growth of the furniture product line industry and IKEA as the one of the
pioneers, manual labour (and its limits of stability and high cost) is increasingly being …