Device-free activity recognition plays a crucial role in smart building, security, and human–
computer interaction, which shows its strength in its convenience and cost-efficiency …

WiFi sensing has been evolving rapidly in recent years. Empowered by propagation models
and deep learning methods, many challenging applications are realized such as WiFi-based …

Over the recent years, WiFi sensing has been rapidly developed for privacy-preserving,
ubiquitous human-sensing applications, enabled by signal processing and deep-learning …

Deep neural networks (DNNs) have made significant advances in computer vision and
sensor-based smart sensing. DNNs achieve prominent results based on standard data sets …

In this work, we propose a simple but effective channel pruning framework called
Progressive Channel Pruning (PCP) to accelerate Convolutional Neural Networks (CNNs) …

Abstract Model compression has been widely adopted to obtain light-weighted deep neural
networks. Most prevalent methods, however, require fine-tuning with sufficient training data …

Deep models are notoriously known to perform poorly when encountering new domains with
different statistics. To alleviate this issue, we present a new domain generalization method …

This paper focuses on the domain generalization task where domain knowledge is
unavailable, and even worse, only samples from a single domain can be utilized during …

Knowledge distillation (KD) transfers discriminative knowledge from a large and complex
model (known as teacher) to a smaller and faster one (known as student). Existing advanced …

Vanilla unsupervised domain adaptation methods tend to optimize the model with fixed
neural architecture, which is not very practical in real-world scenarios since the target data is …