Relation of Activity and Confidence When Training Deep Neural Networks

Valerie Krug, Christopher Olson, Sebastian Stober

Abstract: Deep Neural Networks (DNNs) are successful but work as black-boxes. Elucidating their inner workings is crucial but a difficult task. In this work, we investigate how activity and confidence of a DNN relate in a simple Multi-Layer Perceptron. Further, we observe how activity, confidence and their relation develop during model training. For ease of visual comparison, we use a technique to display DNN activity as topographic maps, similar to common visualization of brain activity. Our results indicate that activity becomes stronger and distinguished both with training time and confidence.

External IDs:doi:10.1007/978-3-031-74627-7_27