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README.md

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-# HIR
-Matlab code for the paper "Hierarchical invariance for robust and interpretable vision tasks at larger scales"
+# Hierarchical Invariant Representation
+This repository is an implementation of the method in  
+"Hierarchical invariance for robust and interpretable vision tasks at larger scales", *Under review*, 2024.  
+Code implemented by Shuren Qi ( i@srqi.email ). All rights reserved.
+
+## Overview
+
+Developing robust and interpretable vision systems is a crucial step towards trustworthy artificial intelligence. In this regard,
+a promising paradigm considers embedding task-required invariant structures, e.g., geometric invariance, in the fundamental image
+representation. However, such invariant representations typically exhibit limited discriminability, limiting their applications in larger-scale
+trustworthy vision tasks. For this open problem, we conduct a systematic investigation of hierarchical invariance, exploring this topic
+from theoretical, practical, and application perspectives. At the theoretical level, we show how to construct over-complete invariants
+with a Convolutional Neural Networks (CNN)-like hierarchical architecture yet in a fully interpretable manner. The general blueprint,
+specific definitions, invariant properties, and numerical implementations are provided. At the practical level, we discuss how to
+customize this theoretical framework into a given task. With the over-completeness, discriminative features w.r.t. the task can be
+adaptively formed in a Neural Architecture Search (NAS)-like manner. We demonstrate the above arguments with accuracy, invariance,
+and efficiency results on texture, digit, and parasite classification experiments. Furthermore, at the application level, our
+representations are explored in real-world forensics tasks on adversarial perturbations and Artificial Intelligence Generated Content
+(AIGC). Such applications reveal that the proposed strategy not only realizes the theoretically promised invariance, but also exhibits
+competitive discriminability even in the era of deep learning. For robust and interpretable vision tasks at larger scales, hierarchical
+invariant representation can be considered as an effective alternative to traditional CNN and invariants.