# Semantically Interpretable and Controllable Filter Sets

## TL;DR

This paper introduces a method for generating semantically interpretable filters from natural images in an unsupervised way, enabling controllable sensitivity for applications like image recognition and quality assessment.

## Contribution

It presents a novel approach to learn interpretable filter sets that can be tuned for different tasks, demonstrating state-of-the-art results on recognition and quality assessment datasets.

## Key findings

- Achieves state-of-the-art performance on CURE-TSR and TID 2013 datasets.
- Demonstrates robustness of filters across progressive distortions.
- Enables controllable sensitivity to color degradations.

## Abstract

In this paper, we generate and control semantically interpretable filters that are directly learned from natural images in an unsupervised fashion. Each semantic filter learns a visually interpretable local structure in conjunction with other filters. The significance of learning these interpretable filter sets is demonstrated on two contrasting applications. The first application is image recognition under progressive decolorization, in which recognition algorithms should be color-insensitive to achieve a robust performance. The second application is image quality assessment where objective methods should be sensitive to color degradations. In the proposed work, the sensitivity and lack thereof are controlled by weighing the semantic filters based on the local structures they represent. To validate the proposed approach, we utilize the CURE-TSR dataset for image recognition and the TID 2013 dataset for image quality assessment. We show that the proposed semantic filter set achieves state-of-the-art performances in both datasets while maintaining its robustness across progressive distortions.