Self-supervised learning: A succinct review
Abstract Machine learning has made significant advances in the field of image processing.
The foundation of this success is supervised learning, which necessitates annotated labels …
The foundation of this success is supervised learning, which necessitates annotated labels …
Beyond just vision: A review on self-supervised representation learning on multimodal and temporal data
Recently, Self-Supervised Representation Learning (SSRL) has attracted much attention in
the field of computer vision, speech, natural language processing (NLP), and recently, with …
the field of computer vision, speech, natural language processing (NLP), and recently, with …
Densely knowledge-aware network for multivariate time series classification
Multivariate time series classification (MTSC) based on deep learning (DL) has attracted
increasingly more research attention. The performance of a DL-based MTSC algorithm is …
increasingly more research attention. The performance of a DL-based MTSC algorithm is …
Cocoa: Cross modality contrastive learning for sensor data
Self-Supervised Learning (SSL) is a new paradigm for learning discriminative
representations without labeled data, and has reached comparable or even state-of-the-art …
representations without labeled data, and has reached comparable or even state-of-the-art …
Assessing the state of self-supervised human activity recognition using wearables
The emergence of self-supervised learning in the field of wearables-based human activity
recognition (HAR) has opened up opportunities to tackle the most pressing challenges in the …
recognition (HAR) has opened up opportunities to tackle the most pressing challenges in the …
Human activity recognition (har) using deep learning: Review, methodologies, progress and future research directions
P Kumar, S Chauhan, LK Awasthi - Archives of Computational Methods in …, 2024 - Springer
Human activity recognition is essential in many domains, including the medical and smart
home sectors. Using deep learning, we conduct a comprehensive survey of current state …
home sectors. Using deep learning, we conduct a comprehensive survey of current state …
Flame: Federated learning across multi-device environments
Federated Learning (FL) enables distributed training of machine learning models while
keeping personal data on user devices private. While we witness increasing applications of …
keeping personal data on user devices private. While we witness increasing applications of …
HMGAN: A hierarchical multi-modal generative adversarial network model for wearable human activity recognition
Wearable Human Activity Recognition (WHAR) is an important research field of ubiquitous
and mobile computing. Deep WHAR models suffer from the overfitting problem caused by …
and mobile computing. Deep WHAR models suffer from the overfitting problem caused by …
Transfer learning approach for human activity recognition based on continuous wavelet transform
Over the last few years, human activity recognition (HAR) has drawn increasing interest from
the scientific community. This attention is mainly attributable to the proliferation of wearable …
the scientific community. This attention is mainly attributable to the proliferation of wearable …
Practically Adopting Human Activity Recognition
Existing inertial measurement unit (IMU) based human activity recognition (HAR)
approaches still face a major challenge when adopted across users in practice. The severe …
approaches still face a major challenge when adopted across users in practice. The severe …