The focus of this study is on reducing computational energy by exploiting the concept of transfer learning and energy-efficient ... a convolutional autoencoder is used for learning a generalized ...

This setup lays the groundwork for experiments involving transfer learning on a CNN, where a pre-trained CNN enhanced by an autoencoder helps extract robust features for improved classification ...

In this article, we propose a stack autoencoder transfer learning algorithm based on the class separation and domain fusion (SAE-CSDF) to solve these problems. According to the characteristics of ...

This notebook deals with Transfer Learning on Stanford's STL-10 dataset using Google Inception Model and also the designing a Convolutional Autoencoder (CAE) and training it learn the features of the ...

In this paper, we present an mTBI-identification model using cross-subject transfer learning and adversarial networks. The proposed method consists of a long short-term memory-based variational ...

Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...