An Introduction to Deep Visual Explanation

By Housam Khalifa Bashier Babiker, Randy Goebel. 2017

19 citations recorded by the source corpus.

The practical impact of deep learning on complex supervised learning problems has been significant, so much so that almost every Artificial Intelligence problem, or at least a portion thereof, has been somehow recast as a deep learning problem. The applications appeal is significant, but this appeal is increasingly challenged by what some call the challenge of explainability, or more generally the more traditional challenge of debuggability: if the outcomes of a deep learning process produce unexpected results (e.g., less than expected performance of a classifier), then there is little available in the way of theories or tools to help investigate the potential causes of such unexpected behavior, especially when this behavior could impact people's lives. We describe a preliminary framework to help address this issue, which we call "deep visual explanation" (DVE). "Deep," because it is the development and performance of deep neural network models that we want to understand. "Visual," because we believe that the most rapid insight into a complex multi-dimensional model is provided by appropriate visualization techniques, and "Explanation," because in the spectrum from instrumentation by inserting print statements to the abductive inference of explanatory hypotheses, we believe that the key to understanding deep learning relies on the identification and exposure of hypotheses about the performance behavior of a learned deep model. In the exposition of our preliminary framework, we use relatively straightforward image classification examples and a variety of choices on initial configuration of a deep model building scenario. By careful but not complicated instrumentation, we expose classification outcomes of deep models using visualization, and also show initial results for one potential application of interpretability.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Explainable AI: The New 42? (2018, background): “There are also other approaches to explanation that formulate heuristics for creating what have been called “Deep Visual Explanation” [1].”
Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey (2019, background): “Another approach that focuses on the most discriminative region in an image to explain an automatic decision is Deep Visual Explanation ( DVE ) [81], see Figure 13.”
Causability and explainability of artificial intelligence in medicine (2019, methodology): “For more details on the theory behind scientific explainability we refer to the principles of abductive reasoning (Ma et al., 2010) and point to some current work (Babiker & Goebel, 2017; Goebel et al., 2018).”