Boosting Transformer's Robustness and Efficacy in PPG Signal Artifact Detection with Self-Supervised Learning

• URL: http://arxiv.org/abs/2401.01013v1
• Date: Tue, 2 Jan 2024 04:00:48 GMT
• Title: Boosting Transformer's Robustness and Efficacy in PPG Signal Artifact Detection with Self-Supervised Learning
• Authors: Thanh-Dung Le
• Abstract summary: This study addresses the underutilization of abundant unlabeled data by employing self-supervised learning (SSL) to extract latent features from this data. Our experiments demonstrate that SSL significantly enhances the Transformer model's ability to learn representations. This approach holds promise for broader applications in PICU environments, where annotated data is often limited.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent research at CHU Sainte Justine's Pediatric Critical Care Unit (PICU) has revealed that traditional machine learning methods, such as semi-supervised label propagation and K-nearest neighbors, outperform Transformer-based models in artifact detection from PPG signals, mainly when data is limited. This study addresses the underutilization of abundant unlabeled data by employing self-supervised learning (SSL) to extract latent features from these data, followed by fine-tuning on labeled data. Our experiments demonstrate that SSL significantly enhances the Transformer model's ability to learn representations, improving its robustness in artifact classification tasks. Among various SSL techniques, including masking, contrastive learning, and DINO (self-distillation with no labels)-contrastive learning exhibited the most stable and superior performance in small PPG datasets. Further, we delve into optimizing contrastive loss functions, which are crucial for contrastive SSL. Inspired by InfoNCE, we introduce a novel contrastive loss function that facilitates smoother training and better convergence, thereby enhancing performance in artifact classification. In summary, this study establishes the efficacy of SSL in leveraging unlabeled data, particularly in enhancing the capabilities of the Transformer model. This approach holds promise for broader applications in PICU environments, where annotated data is often limited.

Related papers

• Context-Aware Predictive Coding: A Representation Learning Framework for WiFi Sensing [0.0]
  WiFi sensing is an emerging technology that utilizes wireless signals for various sensing applications. In this paper, we introduce a novel SSL framework called Context-Aware Predictive Coding (CAPC) CAPC effectively learns from unlabelled data and adapts to diverse environments. Our evaluations demonstrate that CAPC not only outperforms other SSL methods and supervised approaches, but also achieves superior generalization capabilities.
  arXiv  Detail & Related papers  (2024-09-16T17:59:49Z)
• Reinforcement Learning-Guided Semi-Supervised Learning [20.599506122857328]
  We propose a novel Reinforcement Learning Guided SSL method, RLGSSL, that formulates SSL as a one-armed bandit problem. RLGSSL incorporates a carefully designed reward function that balances the use of labeled and unlabeled data to enhance generalization performance. We demonstrate the effectiveness of RLGSSL through extensive experiments on several benchmark datasets and show that our approach achieves consistent superior performance compared to state-of-the-art SSL methods.
  arXiv  Detail & Related papers  (2024-05-02T21:52:24Z)
• Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix [59.55173022987071]
  We study the potential of semi-supervised learning for class-agnostic motion prediction. Our framework adopts a consistency-based self-training paradigm, enabling the model to learn from unlabeled data. Our method exhibits comparable performance to weakly and some fully supervised methods.
  arXiv  Detail & Related papers  (2023-12-13T09:32:50Z)
• Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities [50.231837687221685]
  Self-supervised learning (SSL) has transformed machine learning and its many real world applications. Unsupervised anomaly detection (AD) has also capitalized on SSL, by self-generating pseudo-anomalies.
  arXiv  Detail & Related papers  (2023-08-28T07:55:01Z)
• Robust Learning with Progressive Data Expansion Against Spurious Correlation [65.83104529677234]
  We study the learning process of a two-layer nonlinear convolutional neural network in the presence of spurious features. Our analysis suggests that imbalanced data groups and easily learnable spurious features can lead to the dominance of spurious features during the learning process. We propose a new training algorithm called PDE that efficiently enhances the model's robustness for a better worst-group performance.
  arXiv  Detail & Related papers  (2023-06-08T05:44:06Z)
• Augmentation-aware Self-supervised Learning with Conditioned Projector [6.720605329045581]
  Self-supervised learning (SSL) is a powerful technique for learning from unlabeled data. We propose to foster sensitivity to characteristics in the representation space by modifying the projector network. Our approach, coined Conditional Augmentation-aware Self-supervised Learning (CASSLE), is directly applicable to typical joint-embedding SSL methods.
  arXiv  Detail & Related papers  (2023-05-31T12:24:06Z)
• Rethinking Evaluation Protocols of Visual Representations Learned via Self-supervised Learning [1.0499611180329804]
  Self-supervised learning (SSL) is used to evaluate the quality of visual representations learned via self-supervised learning (SSL) Existing SSL methods have shown good performances under those evaluation protocols. We try to figure out the cause of performance sensitivity by conducting extensive experiments with state-of-the-art SSL methods.
  arXiv  Detail & Related papers  (2023-04-07T03:03:19Z)
• Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability [3.8036939971290007]
  We propose a Self-Supervised Learning (SSL) method with a pretext task of signal reconstruction to learn an informative generalized PPG representation. Results show that in a very limited label data setting (10 samples per class or less), using SSL is beneficial. SSL may pave the way for the broader use of machine learning models on PPG data in label-scarce regimes.
  arXiv  Detail & Related papers  (2022-12-07T19:02:45Z)
• Improving the Adversarial Robustness of NLP Models by Information Bottleneck [112.44039792098579]
  Non-robust features can be easily manipulated by adversaries to fool NLP models. In this study, we explore the feasibility of capturing task-specific robust features, while eliminating the non-robust ones by using the information bottleneck theory. We show that the models trained with our information bottleneck-based method are able to achieve a significant improvement in robust accuracy.
  arXiv  Detail & Related papers  (2022-06-11T12:12:20Z)
• Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning [60.26659373318915]
  Active learning (AL) and semi-supervised learning (SSL) are two effective, but often isolated, means to alleviate the data-hungry problem. We propose an innovative Inconsistency-based virtual aDvErial algorithm to further investigate SSL-AL's potential superiority. Two real-world case studies visualize the practical industrial value of applying and deploying the proposed data sampling algorithm.
  arXiv  Detail & Related papers  (2022-06-07T13:28:43Z)
• CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning [56.20123080771364]
  We develop a model-agnostic Contrastive-Curiosity-Driven Learning Framework (CCLF) for reinforcement learning. CCLF fully exploit sample importance and improve learning efficiency in a self-supervised manner. We evaluate this approach on the DeepMind Control Suite, Atari, and MiniGrid benchmarks.
  arXiv  Detail & Related papers  (2022-05-02T14:42:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.