Hierarchical Semantic Tree Concept Whitening for Interpretable Image Classification

Understand

With the popularity of deep neural networks (DNNs), model interpretability is becoming a critical concern.

• Many approaches have been developed to tackle the problem through post-hoc analysis, such as explaining how predictions are made or understanding the meaning of neurons in middle layers.
• Nevertheless, these methods can only discover the patterns or rules that naturally exist in models.
• In this work, rather than relying on post-hoc schemes, we proactively instill knowledge to alter the representation of human-understandable concepts in hidden layers.