Hence, age is one of the factors that decreases the accuracy of face recognition. Face aging, or age progression, is thus a significant challenge in face recognition methods. This paper presents the use of artificial neural network with model-agnostic meta-learning (ANN-MAML)...
Machine learning, Deep learning, Computer vision, Multimodal models, Neural network models of cognitive functions, Cognitive computation, Deep learning for sentiment analysis Maryam Parsa George Mason University Department of Electrical and Computer Engineering, Fairfax, Virginia, United States ...
TD, and CTD are simple DNN models. The only differences among them are the input features that each model used. CD + CNN, TD + CNN, CTD + CNN, and D + CNN are neural network models with complicated structures. They consist of three main parts: the DNN part, the...
This paper presents a novel approach for synthesizing facial affect; either in terms of the six basic expressions (i.e., anger, disgust, fear, joy, sadness
We focus on the emergent refractory CCA, Nb–Mo–Ta, as the study system to demonstrate the neural network kinetics (NNK) scheme. When generating diffusion datasets for training the neural networks, we use atomic models consisting of 2000 atoms. To compute the vacancy diffusion energy barriers ...
These characteristics make biomimetic platforms favorable for electrophysiological recording and stimulation. The electrical conductivity of hydrogel-based electronics depends on the percolative network of the conductive fillers within the bulk of the hydrogels, the intrinsic electrical conductivity of polymeric ...
In our pursuit of optimizing the performance of our neural network models for pedestrian crossing analysis, we extended our exploration beyond the initially chosen ResNet-50 architecture. Recognizing the advancements in neural network designs, we included two additional models in our comparative study: ...
2018), deep learning models have also gained quantum advantages for image classification (Sagingalieva et al. 2025; Mari et al. 2020). One of the widely accepted deep learning approaches in image processing is the convolution neural network (CNN) (Krizhevsky et al. 2012). Inspired by the...
Within the realm of AutoML, Neural Architecture Search (NAS) has emerged as a powerful technique that automates the design of neural network architectures, the core components of ML models. It has recently gained significant attraction due to its capability to discover novel and efficient ...
In the pre-training step, the neural network acquires knowledge from a large amount of source domain data. In the fine-tuning step, the neural network is initialised with the well-trained weights and biases from pre-trained models. During the fine-tuning stage, some or all layers in the ...