Showing papers by "Michael J. Pazzani published in 2018"

CDeepEx: Contrastive Deep Explanations

Abstract: We propose a method which can visually explain the classification decision of deep neural networks (DNNs). Many methods have been proposed in machine learning and computer vision seeking to clarify the decision of machine learning black boxes, specifically DNNs. All of these methods try to gain insight into why the network “chose class A” as an answer. Humans search for explanations by asking two types of questions. The first question is, “Why did you choose this answer?” The second question asks, “Why did you not choose answer B over A?” The previously proposed methods are not able to provide the latter directly or efficiently. We introduce a method capable of answering the second question both directly and efficiently. In this work, we limit the inputs to be images. In general, the proposed method generates explanations in the input space of any model capable of efficient evaluation and gradient evaluation. It does not require any knowledge of the underlying classifier nor use heuristics in its explanation generation, and it is computationally fast to evaluate. We provide extensive experimental results on three different datasets, showing the robustness of our approach, and its superiority for gaining insight into the inner representations of machine learning models. As an example, we demonstrate our method can detect and explain how a network trained to recognize hair color actually detects eye color, whereas other methods cannot find this bias in the trained classifier.

Explaining Contrasting Categories.

Abstract: This paper describes initial progress in deep learning capable not only of fine-grained categorization tasks, such as whether an image of bird is a Western Grebe or a Clark’s Grebe, but also explaining contrasts to make them understandable. Knowledge-discovery in databases has been described as the process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data [1]. In spite of this, much of machine learning has focused on “valid” and “useful” with little attention paid to “understandable” [26]. Recent work in deep learning has showed remarkable accuracy on a wide range of tasks [7], but produces models that are more difficult to interpret than most earlier approaches to artificial intelligence and machine learning. Our ultimate goal is to learn to annotate images to explain the difference between contrasting categories as found in bird guides or medical books. Author