Description: Block-based processing is a technique used in convolutional neural networks (CNNs) that involves dividing images or data into smaller sections, known as blocks. This approach allows the model to process each block independently, improving efficiency and overall system performance. By working with blocks, CNNs can focus on local features of the image, facilitating the detection of patterns and details that might be lost in a global analysis. Additionally, block processing allows for better memory management and more efficient use of computational resources, as only the necessary parts of the data can be loaded and processed instead of the entire image at once. This technique is particularly useful in applications dealing with large volumes of data, such as computer vision and image recognition. In summary, block-based processing is a key strategy that optimizes the performance of convolutional neural networks by enabling more granular and efficient data analysis.
