Towards Robustness: Enhancing Deep Learning Models through Meta-Learning and Bilevel Optimization for Accurate Car Damage Classification

The article focuses on the difficulties of training deep learning models for real-world scenarios, where the data is often noisy and improperly labeled. Despite the progress made in developing sophisticated models, they require extensive, well-labeled data sets to perform optimally. To tackle this challenge, various approaches have been proposed for training with noisy data, including our previous work, Co-Meta, which proposed a meta-learning approach. In this study, we employed Co-Meta to train a damage classification model with a noisy dataset. We demonstrate how the selection of meta data sets can significantly influence the results and present an efficient method for addressing this issue. Furthermore, we tackle the problem of high memory usage associated with Co-Meta by utilizing alternative bi-level optimization algorithms that decrease the computational resources needed.