From ba4f2b2294752ae80e0ec545a078d39b76aec384 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Shuren=20Qi=20=28=E7=A5=81=E6=A0=91=E4=BB=81=29?= Date: Fri, 23 Feb 2024 15:37:13 +0800 Subject: [PATCH] Update README.md --- README.md | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 69a7aaa..05fc3e3 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,21 @@ -# 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.