Deep neural networks perform incredibly well on computer vision tasks such as classification, object detection, and segmentation, but what do they consider before performing these tasks, and what does it take to make these decisions?
Interpreting neural network decision-making is an ongoing area of research, and it’s quite an important concept to understand. Neural networks are used in the real-world, so we can’t treat them like black boxes—we need to learn what they interpret, how they interpret, and what information each layer/channel in a neural network has learned.
Continue reading Class activation maps: Visualizing neural network decision-making