Deep learning models are widely being employed for object detection due to their high
performance. However, the majority of applications that require object detection are …

Data-free quantization can potentially address data privacy and security concerns in model
compression and thus has been widely investigated. Recently, patch similarity aware data …

Quantization is a critical technique employed across various research fields for compressing
deep neural networks (DNNs) to facilitate deployment within resource-limited environments …

We present CLAMP-ViT, a data-free post-training quantization method for vision
transformers (ViTs). We identify the limitations of recent techniques, notably their inability to …

Filter pruning is one of the most popular approaches for compressing convolutional neural
networks (CNNs). The most critical task in pruning is to evaluate the importance of each …

Remote sensing (RS) scene classification based on deep neural networks (DNNs) has
recently drawn remarkable attention. However, the DNNs contain a great number of …

The concept of randomized neural networks (RNNs), such as the random vector functional
link network (RVFL) and extreme learning machine (ELM), is a widely accepted and efficient …

Machine learning techniques, particularly those based on neural networks, are always more
often used at the edge of the network by Internet of Things (IoT) nodes. Unfortunately, the …

Deep-learning based periocular recognition systems typically use overparameterized deep
neural networks associated with high computational costs and memory requirements. This is …

Current periocular and face recognition approaches utilize computationally costly deep
neural networks, achieving notable recognition accuracies. Deploying such solutions in …