They are developing a Quantum Convolutional Neural Network (QCNN) architecture to enhance the performance of traditional computer vision tasks using quantum mechanics principles.

As the title suggests, this paper studies various different deep convolutional neural network architectures and various techniques to use these CNNs for CADe (Computer Aided Detection) tasks. With the ...

Researchers at Soongsil University (Korea) published “A Survey on Efficient Convolutional Neural Networks and Hardware Acceleration.” Abstract: “Over the past decade, deep-learning-based ...

Convolutional Neural Networks (CNN) are mainly used for image recognition. The fact that the input is assumed to be an image enables an architecture to be created such that certain properties can be ...

In this post, I will briefly review the deep learning architectures that help computers detect objects. Convolutional neural networks. One of the key components of most deep learning–based ...

Convolutional Neural Networks. Convolutional neural networks (CNNs) are a type of neural network that is designed to capture increasingly more complex features within its input data.To do this ...

MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, they are researching CV-QNN (Continuous Variable Quantum Neural Networks) technology, with the ...