Epi-NAF: Enhancing Neural Attenuation Fields for Limited-Angle CT with Epipolar Consistency Conditions

Neural field methods, initially successful in the inverse rendering domain, have recently been extended to CT reconstruction, marking a paradigm shift from traditional techniques. While these approaches deliver state-of-the-art results in sparse-view CT reconstruction, they struggle in limited-angle settings, where input projections are captured over a restricted angle range. We present a novel loss term based on consistency conditions between corresponding epipolar lines in X-ray projection images, aimed at regularizing neural attenuation field optimization. By enforcing these consistency conditions, our approach, Epi-NAF, propagates supervision from input views within the limited-angle range to predicted projections over the full cone-beam CT range. This loss results in both qualitative and quantitative improvements in reconstruction compared to baseline methods.

Anytime Generation of Counterfactual Explanations for Text Classification

In many machine learning applications, it is important for the user to understand the reasoning behind the recommendation or prediction of the classifiers. The learned models, however, are often too complicated to be understood by a human. Research from the social sciences indicates that humans prefer counterfactual explanations over alternatives. In this paper, we present a general framework for generating counterfactual explanations in the textual domain. Our framework is model-agnostic, representation-agnostic, domain-agnostic, and anytime. We model the task as a search problem in a space where the initial state is the classified text, and the goal state is a text in the complementary class. The operators transform a text by replacing parts of it. Our framework includes domain-independent operators, but can also exploit domain-specific knowledge through specialized operators. The search algorithm attempts to find a text from the complementary class with minimal word-level Levenshtein distance from the original classified object.