Core Concepts
Few-shot learning methods can augment AI models with human-like capabilities to learn new tasks with limited examples, overcoming the scarcity of labeled data in biomedical time series applications.
Abstract
This survey provides a comprehensive review and comparison of few-shot learning methods for biomedical time series applications. The key highlights are:
Few-shot learning problems are defined by the limited number of labeled samples available, in contrast to traditional machine learning pipelines that divide datasets into training, validation, and test subsets. Few-shot learning aims to leverage past experiences to learn new tasks with few examples.
The taxonomy of few-shot learning methods includes data-based, model-based, metric-based, optimization-based, and hybrid approaches. Data-based methods generate synthetic samples to expand the support set, model-based methods design specialized architectures for few-shot generalization, metric-based methods learn similarity metrics between samples, and optimization-based methods guide model convergence to parameter spaces that can be quickly adapted.
Few-shot learning methods have been applied to a wide range of biomedical time series applications, including seizure detection, emotion recognition, arrhythmia classification, hand gesture recognition, and more. These methods demonstrate improved performance compared to traditional supervised learning, especially when dealing with data scarcity, class imbalance, and inter-subject variability.
Key challenges include designing effective embedding networks and similarity metrics, handling noisy or mislabeled data, and transferring knowledge across diverse datasets and tasks. Future directions involve exploring self-supervised pre-training, meta-learning, and hybrid approaches to further enhance the few-shot learning capabilities.
Stats
"Accessing extensively labeled datasets to train data-hungry deep learning models encounters many barriers, such as long-tail distribution of rare diseases, cost of annotation, privacy and security concerns, data-sharing regulations, and ethical considerations."
"Few-shot learning emerges as a promising paradigm to augment models with capabilities to generalize effectively even when confronted with a scarcity of labeled data."
Quotes
"Few-shot learning setups are limited by the number of labeled data available and cannot afford such a division. Models trained on small datasets may memorize the specific samples instead of learning the patterns and generalizing them beyond the training set."
"Few-shot learning aims to leverage the information available from the support set S to estimate the label of each query sample from the query set Q."
"Few-shot learning problems can be characterized based on the type of knowledge transfer a model is expected to bridge between the pre-training problem and the N-way-K-shot problem."