Deep neural networks for image super-resolution (SR) have demonstrated superior performance. However, the large memory and computation consumption hinders their deployment on resource-constrained ...

This paper presents the Cellular Binary Neural Network (CBNN), which is an efficient deep neural network with binary weights and activations. To address the challenge of performance drop caused by low ...

To provide a lightweight and cost-effective solution for long-wave infrared imaging using a singlet, we developed a neural network-enhanced metalens camera by integrating a high-frequency-enhancing ...

This is the official code for our paper: How to build the best medical image segmentation algorithm using foundation models: a comprehensive empirical study with Segment Anything Model, where we ...

This study’s research area is artificial intelligence (AI) and machine learning, specifically focusing on neural networks that can understand binary code. The aim is to automate reverse engineering ...

This is the primary motivation and novelty of our design. To the best of our knowledge, it is the first such design on U-shaped spiking networks, as well as for image segmentation. We adopt a modified ...

The citation network allowed us to overcome the following limitations of traditional recommendation systems : Reduce the space and time complexity of recommendation process, so instead of calculating ...