Trustworthy Deep Learning

Trustworthy deep learning aims to address the limitations of deep learning models, particularly their "black box" nature and vulnerability to adversarial attacks, by enhancing their reliability, interpretability, and robustness. Current research focuses on methods like conformal prediction, Shapley value-based data valuation, and techniques to improve model robustness against noisy labels and out-of-distribution data, often employing attention mechanisms, generative adversarial networks, and specialized architectures like spiking neural networks. These advancements are crucial for deploying deep learning models responsibly in high-stakes applications such as healthcare and finance, where transparency and reliability are paramount.