This paper proposes a novel hybrid model that synergistically integrates Variational Autoencoders (VAEs) and Speeded-Up Robust Features (SURF) to address these challenges. The VAE component captures ...

An autoencoder is a type of ... meaningful and interpretable features. Variational Autoencoders (VAEs): A more advanced form that introduces probabilistic elements, allowing for more diverse and ...

Generally, virtual sample generation (VSG) methods such as variational autoencoder (VAE ... individual samples are generated according to the original data distribution. To improve the data ...

After preprocessing, a file named data.tsv is generated to store the processed data. This file serves as input for both the Variational Autoencoder (VAE ... validate improvements arising from our VAE ...

In this article, we propose a self-augmentation strategy for improving ML-based device modeling using variational autoencoder (VAE)-based techniques. These techniques require a small number of ...

In this paper, we propose a strategy for improving ML-based device modeling by data self-augmentation using variational autoencoder-based techniques, where initially only a few experimental data ...

Data augmentation aims to prevent the overfitting of the ... To obtain the expected generated data, a variational autoencoder (VAE) is proposed to improve the performance of the autoencoder. Compared ...

Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few ...