XOC: Explainable Observer-Classifier for Explainable Binary Decisions

By Stephan Alaniz, Zeynep Akata. 2019

16 citations recorded by the source corpus.

Explanations help develop a better understanding of the rationale behind the predictions of a deep neural network and improve trust. We propose an explainable observer-classifier framework that exposes the steps taken through the decision-making process in a transparent manner. Instead of assigning a label to an image in a single step, our model makes iterative binary sub-decisions, and as a byproduct reveals a decision tree in the form of an introspective explanation. In addition, our model creates rationalizations as it assigns each binary decision a semantic meaning in the form of attributes imitating human-annotations. On six benchmark datasets with increasing size and granularity, our model outperforms classical decision-trees and generates easy-to-understand binary decision sequences explaining the network's predictions.

Subfields: Natural Language Processing

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Citations in the corpus

Measuring and Improving BERT’s Mathematical Abilities by Predicting the Order of Reasoning. (2021, background): “…Gulcehre et al., 2016; Malinowski et al., 2018; Xu and Saenko, 2016; Yang et al., 2016), phrase selection (Lei et al., 2016), distillation into programs (Hajipour et al., 2020), or decision trees (Alaniz and Akata, 2019) can potentially increase the transparency of the black-box neural networks.”
EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction (2019, background): “[7] propose visual explanations of a classiﬁer’s decision, while [1] use an architecture composed of an observer and a classiﬁer, in which the classiﬁer’s prediction can be exposed as a binary tree structure.”
Neural Prototype Trees for Interpretable Fine-grained Image Recognition (2020, methodology): “Other tree approaches for image classification apply post-hoc explanation techniques, by showing example images per node [2, 55], visualizing typical CNN filters of each node that can be manually labelled [55], showing class activation maps [25] or manual inspection of leaf labels and the meaning of”
Conservative Q-Improvement: Reinforcement Learning for an Interpretable Decision-Tree Policy (2019, background): “Decision trees are frequently used to explain processes to humans, such as when a deep network is trained to explain itself [Alaniz and Akata, 2019; Huk Park et al., 2018].”
NBDT: Neural-Backed Decision Trees (2020, methodology): “Our baselines are either taken directly from the original papers or improved using a modern backbone: Deep Neural Decision Forest (DNDF updated with ResNet18) [16], Explainable Observer-Classifier (XOC) [2], Deep Convolutional Decision Jungle (DCDJ) [3], Network of Experts (NofE) [1], Deep Decision ”