What is the process of sliding a kernel across an image called?

The process of sliding a kernel across an image is referred to as convolution. In image processing and neural networks, particularly in the context of Convolutional Neural Networks (CNNs), convolution involves applying a filter (the kernel) to different sections of the image to extract features. This operation allows the model to capture important patterns, textures, and edges within the image by combining the pixel values with the corresponding weights in the kernel.

When a kernel is convolved with an image, each position where the kernel is applied results in a new pixel value in the output feature map. This transformation helps the network learn spatial hierarchies and contribute to overall feature detection, which is essential for tasks like image classification, object detection, and segmentation.

The other options relate to different concepts. Pooling refers to reducing the dimensionality of the feature maps, translation often relates more to shifting data rather than applying specific filters, and loading doesn't pertain to image processing techniques. Therefore, the term that accurately describes sliding a kernel across an image is convolution.