Description: Patch-based refers to methods that analyze small regions (patches) of input data, rather than processing the entire image or dataset at once. This technique is especially relevant in the context of convolutional neural networks (CNNs), where the goal is to extract local features from the data. By dividing the input into patches, the model can focus on specific details, which can enhance accuracy in tasks such as image classification, segmentation, and object detection. Patches can vary in size and can overlap, providing the model with a richer and more varied view of the information. This strategy also helps reduce computational complexity, as it allows for processing only a portion of the data at a time, making it easier to handle large volumes of information. Additionally, the patch-based approach is fundamental for techniques like transfer learning, where features learned from patches in one dataset are used to improve performance in another. In summary, the patch-based method is a powerful tool in the field of machine learning, enabling more detailed and efficient analysis of input data.
