Semantics for Global and Local Interpretation of Deep Neural Networks

Understand

Deep neural networks (DNNs) with high expressiveness have achieved state-of-the-art performance in many tasks.

• However, their distributed feature representations are difficult to interpret semantically.
• In this work, human-interpretable semantic concepts are associated with vectors in feature space.
• The association process is mathematically formulated as an optimization problem.