Story

The article discusses the concept of the 'Likelihood-Based Distortion Loss' (LBDL), a method for training deep neural networks that aims to optimize the network's performance on a specific task while minimizing the distortion introduced to the input data. The LBDL approach is presented as a promising technique for improving the accuracy and robustness of deep learning models.