The primary goal of this project was to explore the use of deep learning, specifically an autoencoder, for generating pseudo-random data that exhibits similar characteristics to a given training ...

CNN_AE_helper.py Provides training‐and‐validation loops, checkpoint saving, and optional noise injection or gradient clipping for 3D autoencoders. Functions like train_one_epoch, validate, and ...

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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 ...

Abstract: The use of heart rate monitoring devices has increased significantly, leading to the emergence of a new class of consumer-grade wearable devices designed for continuous heart rate monitoring ...