# Reproducibility study of "LICO: Explainable Models with Language-Image Consistency"

Source: [Reproducibility study of "LICO: Explainable Models with Language-Image Consistency"](<https://www.sota2.com/research/paper/68fa753d1e209131cea66ac8>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:30:07.152Z

Authors: Luan Fletcher, Robert van der Klis, Martin Sedl\\'a\\v{c}ek, Stefan Vasilev, Christos Athanasiadis

Published: 2024-10-17T19:41:34.000Z

## Abstract

The growing reproducibility crisis in machine learning has brought forward a need for careful examination of research findings. This paper investigates the claims made by Lei et al. (2023) regarding their proposed method, LICO, for enhancing post-hoc interpretability techniques and improving image classification performance. LICO leverages natural language supervision from a vision-language model to enrich feature representations and guide the learning process. We conduct a comprehensive reproducibility study, employing (Wide) ResNets and established interpretability methods like Grad-CAM and RISE. We were mostly unable to reproduce the authors' results. In particular, we did not find that LICO consistently led to improved classification performance or improvements in quantitative and qualitative measures of interpretability. Thus, our findings highlight the importance of rigorous evaluation and transparent reporting in interpretability research.

## 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](<https://arxiv.org/abs/2410.13989>)

[PDF](<https://arxiv.org/pdf/2410.13989.pdf>)