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Found 9 papers, 4 papers with code
A collection of the accepted papers for the Human-Centric Representation Learning workshop at AAAI 2024
This non-archival index is not complete, as some accepted papers chose to opt-out of inclusion.
Balancing Continual Learning and Fine-tuning for Human Activity Recognition
These schemes re-purpose contrastive learning for knowledge retention and, Kaizen combines that with self-training in a unified scheme that can leverage unlabelled and labelled data for continual learning.
Kaizen: Practical Self-supervised Continual Learning with Continual Fine-tuning
Kaizen is able to balance the trade-off between knowledge retention and learning from new data with an end-to-end model, paving the way for practical deployment of continual learning systems.
Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering
Federated learning is generally used in tasks where labels are readily available (e. g., next word prediction).
Improving Feature Generalizability with Multitask Learning in Class Incremental Learning
Many deep learning applications, like keyword spotting, require the incorporation of new concepts (classes) over time, referred to as Class Incremental Learning (CIL).
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition
In this paper, we extend this line of research and present a novel technique called Collaborative Self-Supervised Learning (ColloSSL) which leverages unlabeled data collected from multiple devices worn by a user to learn high-quality features of the data.
Evaluating Contrastive Learning on Wearable Timeseries for Downstream Clinical Outcomes
Vast quantities of person-generated health data (wearables) are collected but the process of annotating to feed to machine learning models is impractical.
SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data
Machine learning and deep learning have shown great promise in mobile sensing applications, including Human Activity Recognition.
Exploring Contrastive Learning in Human Activity Recognition for Healthcare
Human Activity Recognition (HAR) constitutes one of the most important tasks for wearable and mobile sensing given its implications in human well-being and health monitoring.
