Wednesday, 13th January, 2021 15:00 CET HCAI Research Seminar
Title: Concept Whitening for Interpretable Image Recognition
Speaker: Zhi CHEN, Duke University, Durham (NC), USA
Abstract: What does a neural network encode about a concept as we traverse through the layers? Interpretability in machine learning is undoubtedly important, but the calculations of neural networks are very challenging tounderstand. Attempts to see inside their hidden layers can either be misleading, unusable, or rely on the latent space to possess properties that it may not have. In this work, rather than attempting to analyze a neural network posthoc, we introduce a mechanism, called concept whitening (CW), to alter a given layer of the network to allow us to better understand the computation leading up to that layer. When a concept whitening module is added to a CNN, the axes of the latent space are aligned with known concepts of interest. By experiment, we show that CW can provide us a much clearer understanding for how the network gradually learns concepts over layers. CW is an alternative to a batch normalization layer in that it normalizes, and also decorrelates (whitens) the latent space. CW can be used in any layer of the network without hurting predictive performance.
Bio: Zhi CHEN is a third year Duke CS PhD student advised by Prof. Cynthia Rudin in her Prediction Analysis Lab at Duke Computer Science . His research interests lie in the interpretability of machine learning, with a focus on building models that are inherently interpretable.
