[HTML][HTML] Deep learning and transfer learning for device-free human activity recognition: A survey
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 …
computer interaction, which shows its strength in its convenience and cost-efficiency …
Dual mixup regularized learning for adversarial domain adaptation
Recent advances on unsupervised domain adaptation (UDA) rely on adversarial learning to
disentangle the explanatory and transferable features for domain adaptation. However …
disentangle the explanatory and transferable features for domain adaptation. However …
Model-agnostic boundary-adversarial sampling for test-time generalization in few-shot learning
Few-shot learning is an important research problem that tackles one of the greatest
challenges of machine learning: learning a new task from a limited amount of labeled data …
challenges of machine learning: learning a new task from a limited amount of labeled data …
SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing
Over the recent years, WiFi sensing has been rapidly developed for privacy-preserving,
ubiquitous human-sensing applications, enabled by signal processing and deep-learning …
ubiquitous human-sensing applications, enabled by signal processing and deep-learning …
Learning gestures from WiFi: A siamese recurrent convolutional architecture
We propose a gesture recognition system that leverages existing WiFi infrastructures and
learns gestures from channel state information (CSI) measurements. Having developed an …
learns gestures from channel state information (CSI) measurements. Having developed an …
Acrofod: An adaptive method for cross-domain few-shot object detection
Y Gao, L Yang, Y Huang, S Xie, S Li… - European Conference on …, 2022 - Springer
Under the domain shift, cross-domain few-shot object detection aims to adapt object
detectors in the target domain with a few annotated target data. There exists two significant …
detectors in the target domain with a few annotated target data. There exists two significant …
Conditional synthetic data generation for robust machine learning applications with limited pandemic data
Background: At the onset of a pandemic, such as COVID-19, data with proper
labeling/attributes corresponding to the new disease might be unavailable or sparse …
labeling/attributes corresponding to the new disease might be unavailable or sparse …
SWL-Adapt: An unsupervised domain adaptation model with sample weight learning for cross-user wearable human activity recognition
Abstract In practice, Wearable Human Activity Recognition (WHAR) models usually face
performance degradation on the new user due to user variance. Unsupervised domain …
performance degradation on the new user due to user variance. Unsupervised domain …
Adversarial learning-enabled automatic WiFi indoor radio map construction and adaptation with mobile robot
Location-based service (LBS) has become an indispensable part of our daily lives.
Realizing accurate LBS in indoor environments is still a challenging task. WiFi fingerprinting …
Realizing accurate LBS in indoor environments is still a challenging task. WiFi fingerprinting …
Feature alignment by uncertainty and self-training for source-free unsupervised domain adaptation
Most unsupervised domain adaptation (UDA) methods assume that labeled source images
are available during model adaptation. However, this assumption is often infeasible owing to …
are available during model adaptation. However, this assumption is often infeasible owing to …