Visualizing the decision-making process in deep neural decision forest

By Shichao Li, Kwang-Ting Cheng. 2019

8 citations recorded by the source corpus.

Deep neural decision forest (NDF) achieved remarkable performance on various vision tasks via combining decision tree and deep representation learning. In this work, we first trace the decision-making process of this model and visualize saliency maps to understand which portion of the input influence it more for both classification and regression problems. We then apply NDF on a multi-task coordinate regression problem and demonstrate the distribution of routing probabilities, which is vital for interpreting NDF yet not shown for regression problems. The pre-trained model and code for visualization will be available at this https URL

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Neural Prototype Trees for Interpretable Fine-grained Image Recognition (2020, background): “Since their prototypes (a) DNDF [32] (b) SDT [14] (c) SDT [21] (d) Ours have smaller spatial dimensions than the image itself, they represent prototypical parts and are therefore suited for ﬁne-grained image classiﬁcation.”
SegNBDT: Visual Decision Rules for Segmentation (2020, background): “Work in this area is limited to [12, 36] with complementary downfalls: Li et al.”