Pixel Augmentation

Pixel augmentation is a data augmentation technique enhancing the training of machine learning models, particularly in image analysis, by modifying individual pixels within images. Current research focuses on developing effective and efficient augmentation strategies, including methods tailored to specific image types (e.g., medical images) and those integrated with various architectures like convolutional neural networks (CNNs) and transformers. These advancements improve model performance in tasks such as semantic segmentation, anomaly detection, and medical image analysis, ultimately leading to more robust and accurate results in diverse applications.