Abstract: Transfer learning has emerged as a powerful technique in machine learning, wherein knowledge acquired from one task or dataset is leveraged to improve performance on related tasks and ...

The proposed algorithm enhances the traditional conventional convolutional neural network (CNN) algorithm by introducing a domain category judgment module and an inter-domain conditional probability ...

MobiCollab enables proximity-based collaborative transfer learning where nearby mobile devices discover each other, share training data, and collectively improve an on-device image classification ...

Learn about DenseNet, one of the most powerful deep learning architectures, in this beginner-friendly tutorial. Understand its structure, advantages, and how it’s used in real-world AI applications.

Abstract: Transfer learning refers to the process of transferring the learning from one task to another related task. Transfer learning is especially beneficial in situations where we have limited ...

ABSTRACT: Douala-Cameroon is a growing city with huge number of cars and motorcycles plying the roads, especially during rush hours. These automobiles emit exhaust gases, leading to atmospheric ...

Professorship of Multiscale Modeling of Fluid Materials, Department of Engineering Physics and Computation, TUM School of Engineering and Design, Technical University of Munich, Garching 85748, ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Biophotonic technologies such as Raman spectroscopy are powerful tools for obtaining ...

AlexLens is a comprehensive Image Classification and Transfer Learning application, specifically designed for heterogeneous computing platforms. It features a custom-built AlexNet Neural Network for ...