Timeclr: A self-supervised contrastive learning framework for univariate time series representation
X Yang, Z Zhang, R Cui - Knowledge-Based Systems, 2022 - Elsevier
Time series are usually rarely or sparsely labeled, which limits the performance of deep
learning models. Self-supervised representation learning can reduce the reliance of deep …
learning models. Self-supervised representation learning can reduce the reliance of deep …
A survey on gan techniques for data augmentation to address the imbalanced data issues in credit card fraud detection
E Strelcenia, S Prakoonwit - Machine Learning and Knowledge Extraction, 2023 - mdpi.com
Data augmentation is an important procedure in deep learning. GAN-based data
augmentation can be utilized in many domains. For instance, in the credit card fraud domain …
augmentation can be utilized in many domains. For instance, in the credit card fraud domain …
Conditional GAN for timeseries generation
It is abundantly clear that time dependent data is a vital source of information in the world.
The challenge has been for applications in machine learning to gain access to a …
The challenge has been for applications in machine learning to gain access to a …
Time-series regeneration with convolutional recurrent generative adversarial network for remaining useful life estimation
For health prognostic task, ever-increasing efforts have been focused on machine learning
based methods, which are capable of yielding accurate remaining useful life (RUL) …
based methods, which are capable of yielding accurate remaining useful life (RUL) …