Reproducibility Study of "Languange-Image COnsistency"

Abstract

This report aims to verify the findings and expand upon the evaluation and training methods from the paper LICO: Explainable Models with Language-Image COnsistency. The main claims from the original paper are that LICO (i) enhances interpretability by producing more explainable saliency maps in conjunction with a post-hoc explainability method and (ii) improves image classification performance without computational overhead during inference. We have reproduced the key experiments conducted by Lei et al.; however, the obtained results do not support the original claims. Additionally, we identify a limitation in the paper’s evaluation method, which favors non-robust models, and propose robust experimental setups for more comprehensive quantitative analysis. Furthermore, we undertake additional studies on LICO’s training methodology to enhance its interpretability. Our code is available at https://github.com/konradszewczyk/lico-reproduction.