Space Augmentation

Space augmentation, a technique enhancing machine learning model performance by manipulating data representations in latent spaces, aims to improve model generalization and robustness, particularly in scenarios with limited or imbalanced data. Current research focuses on developing methods for effective latent space augmentation, including those based on variational autoencoders, and exploring optimal layer selection for applying augmentations within neural networks. These advancements are significant for various applications, such as improving image classification accuracy across diverse domains, enhancing face recognition, and enabling more efficient and effective training of deep learning models in resource-constrained settings.