Optimising for Interpretability: Convolutional Dynamic Alignment Networks

@article{Boehle2021OptimisingFI,
  title={Optimising for Interpretability: Convolutional Dynamic Alignment Networks},
  author={Moritz D Boehle and Mario Fritz and Bernt Schiele},
  journal={IEEE transactions on pattern analysis and machine intelligence},
  year={2021},
  volume={PP}
}
We introduce a new family of neural network models called Convolutional Dynamic Alignment Networks (CoDA Nets), which are performant classifiers with a high degree of inherent interpretability. Their core building blocks are Dynamic Alignment Units (DAUs), which are optimised to transform their inputs with dynamically computed weight vectors that align with task-relevant patterns. As a result, CoDA Nets model the classification prediction through a series of input-dependent linear… 
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