Automatic Concept Embedding Model (ACEM): No train-time concepts, No
  issue!
Rishabh Jain
Interpretability and explainability of neural networks is continuously
increasing in importance, especially within safety-critical domains and to
provide the social right to explanation. Concept based explanations align well
with how humans reason, proving to be a good way to explain models. Concept
Embedding Models (CEMs) are one such concept based explanation architectures.
These have shown to overcome the trade-off between explainability and
performance. However, they have a key limitation -- they require concept
annotations for all their training data. For large datasets, this can be
expensive and infeasible. Motivated by this, we propose Automatic Concept
Embedding Models (ACEMs), which learn the concept annotations automatically.