Explicable Reconstruction Network

Explicable Reconstruction Networks (ERNs) aim to improve the transparency and interpretability of deep learning models, addressing the "black box" problem. Current research focuses on developing ERNs that generate concise, human-understandable explanations for model predictions, often employing techniques like concept relevance propagation and probabilistic undersampling to achieve this. These networks find applications in diverse fields, including image classification, face recognition, and medical imaging, where understanding model decisions is crucial for trust and reliable deployment. The ultimate goal is to build more trustworthy and accountable AI systems by providing insights into their internal workings.