12.6 Tool: keras-salient-object-visualization
Keras implementation of Nvidia paper ‘Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car’. The goal of the visualization is to explain what DonkeyCar (https://github.com/wroscoe/donkey) learns and how it makes its decisions. The central idea in discerning the salient objects is finding parts of the image that correspond to locations where the feature maps of CNN layers have the greatest activations.
The code can be found at GitHub
12.6.1 Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car
- Enable further system improvement
- Create trust that the system is paying attention to the essential cues
The conclusion of the paper is
We describe a method for finding the regions in input images by which PilotNet makes its steering decisions, i. e., the salient objects. We further provide evidence that the salient objects identified by this method are correct. The results substantially contribute to our understanding of what PilotNet learns. Examination of the salient objects shows that PilotNet learns features that “make sense” to a human, while ignoring structures in the camera images that are not relevant to driving. This capability is derived from data without the need of hand-crafted rules. In fact, PilotNet learns to recognize subtle features which would be hard to anticipate and program by human engineers, such as bushes lining the edge of the road and atypical vehicle classes. (Bojarski et al. 2017)
12.6.2 VisualBackProp: efficient visualization of CNNs
The conclusion of the paper is
In this paper we propose a new method for visualizing the regions of the input image that have the highest influence on the output of a CNN. The presented approach is computationally efficient which makes it a feasible method for real-time applications as well as for the analysis of large data sets. We provide theoretical justification for the proposed method and empirically show on the task of autonomous driving that it is a valuable diagnostic tool for CNNs.
(Bojarski et al. 2016)
References
Bojarski, Mariusz, Anna Choromanska, Krzysztof Choromanski, Bernhard Firner, Larry Jackel, Urs Muller, and Karol Zieba. 2016. “Visualbackprop: Efficient Visualization of Cnns.” arXiv Preprint arXiv:1611.05418.
Bojarski, Mariusz, Philip Yeres, Anna Choromanska, Krzysztof Choromanski, Bernhard Firner, Lawrence Jackel, and Urs Muller. 2017. “Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car.” arXiv Preprint arXiv:1704.07911.