Unmasking Clever Hans Predictors and Assessing Machine Learning - Spring 2021

Unmasking Clever Hans Predictors and Assessing Machine Learning - Spring 2021

Explore a comprehensive lecture on unmasking clever Hans predictors and assessing machine learning outcomes. Delve into popular heatmap techniques, Layer-wise Relevance Propagation (LRP), and its characteristics. Learn about LRP Taylor Series Expansion, backpropagation of last layer relevance, and LRP rules. Compare LRP heatmaps to Sensitivity Analysis for accuracy. Discover SPRAY (Spectral Relevance Analysis) and its application in cracking Fisher Vectors vs DNN predictions. Examine aggregate heatmap visualizations for various objects and uncover potential faults in DNN predictions. Gain valuable insights into assessing what machines truly learn and how to interpret their decision-making processes.