Transfer learning is a machine learning technique that uses previously acquired knowledge from a source domain to enhance learning in a target domain by reusing learned weights. This technique is ubiquitous because of its great advantages in achieving high performance while saving training time, memory, and effort in network design. In this paper, we investigate how to select the best pre-trained model that meets the target domain requirements for image classification tasks. In our study, we refined the output layers and general network parameters to apply the knowledge of eleven image processing models, pre-trained on ImageNet, to five different target domain datasets. We measured the accuracy, accuracy density, training time, and model size to evaluate the pre-trained models both in training sessions in one episode and with ten episodes.
In edge-assisted low-latency video analytics, a critical challenge is balancing on-device inference latency against the high bandwidth costs and network delays of offloading. Ineffectively managing this trade-off degrades performance and hinders critical applications like autonom…
The increasing convergence of Operational Technology (OT) and Information Technology (IT) within the Industrial Internet of Things (IIoT) brings about remarkable improvements in monitoring and automation. However, it also exposes industrial systems to large-scale Distributed Deni…
Deep neural networks (DNNs) have significantly advanced machine perception and reasoning; however, their lack of transparency in decision-making continues to pose a major challenge, particularly in high-stakes domains such as healthcare, finance, and law. This is especially conce…
In recent decades, data collection technologies have evolved to facilitate the monitoring and improvement of numerous activities and processes in everyday human life. Their evolution is propelled by the advancement of artificial intelligence (AI), which aims to emulate human inte…
Autism Spectrum Disorder is a long-term neurodevelopmental disorder. Early diagnosis is crucial for timely rehabilitation and intervention. Recently, machine learning and deep learning techniques have been widely explored and have produced encouraging results using eye-tracking s…
Due to their considerable costs, deep neural networks (DNNs) are valuable assets that need to be protected in terms of intellectual property (IP). From this statement, DNN watermarking gains significant interest since it allows DNN owners to prove their ownership. Various methods…