Before ResNet changed everything, this 2015 paper pushed CNNs to new depths and beat human-level performance on ImageNet. The team behind it—led by Kaiming He—showed that with Parametric ReLU and Batch Normalization, deep models could finally be trained efficiently and accurately.

In this episode of Plain Science, we explore how Delving Deep into Rectifiers laid the groundwork for the next wave of breakthroughs in computer vision.

Paper: https://openaccess.thecvf.com/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf