Network Inversion

Network inversion aims to reverse-engineer the internal workings of neural networks, revealing the input features that lead to specific outputs. Current research focuses on developing efficient and robust inversion methods for various network architectures, including convolutional and binarized neural networks, often employing generative adversarial networks (GANs) or invertible neural networks (INNs) to achieve this. This research is crucial for enhancing the interpretability and trustworthiness of neural networks, particularly in high-stakes applications, and also finds applications in areas like image enhancement and inverse problems in fields such as electromagnetic imaging. The ability to effectively invert networks promises to improve both the understanding and practical utility of these powerful models.