Explain to Fix: A Framework to Interpret and Correct DNN Object Detector Predictions

Understand

Explaining predictions of deep neural networks (DNNs) is an important and nontrivial task.

• In this paper, we propose a practical approach to interpret decisions made by a DNN object detector that has fidelity comparable to state-of-the-art methods and sufficient computational efficiency to process large datasets.
• Our method relies on recent theory and approximates Shapley feature importance values.
• We qualitatively and quantitatively show that the proposed explanation method can be used to find image features which cause failures in DNN object detection.