Explore a novel approach to processing large-scale images using deep attention-sampling models in this informative video. Learn how the proposed attention sampling technique enables selective processing of high-resolution image parts while discarding less relevant information, resulting in significant improvements in speed and memory efficiency. Discover how this method allows for the analysis of megapixel images using standard GPU setups, overcoming the limitations of current convolutional neural networks. Gain insights into the unbiased estimation process, gradient calculation, and end-to-end training procedure using stochastic gradient descent. Examine the model's performance across three classification tasks, demonstrating substantial reductions in computational and memory requirements while maintaining accuracy comparable to classical architectures. Understand the consistency of the sampling process and its focus on informative image regions.