# Improving Shape Awareness and Interpretability in Deep Networks Using Geometric Moments Source: [Improving Shape Awareness and Interpretability in Deep Networks Using Geometric Moments]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:19:28.160Z Authors: Rajhans Singh, Ankita Shukla, Pavan Turaga (1) \_\_INSTITUTION\_3\_\_ Arizona State University) Published: 2022-05-24T02:08:05.000Z ## Abstract Deep networks for image classification often rely more on texture information than object shape. While efforts have been made to make deep-models shape-aware, it is often difficult to make such models simple, interpretable, or rooted in known mathematical definitions of shape. This paper presents a deep-learning model inspired by geometric moments, a classically well understood approach to measure shape-related properties. The proposed method consists of a trainable network for generating coordinate bases and affine parameters for making the features geometrically invariant yet in a task-specific manner. The proposed model improves the final feature's interpretation. We demonstrate the effectiveness of our method on standard image classification datasets. The proposed model achieves higher classification performance compared to the baseline and standard ResNet models while substantially improving interpretability. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()