Explore a video explanation of the Weight Standardization paper, which introduces a novel approach to accelerate deep network training. Learn how this technique extends normalization to include network weights, leading to performance improvements and new state-of-the-art results when combined with GroupNorm. Discover the benefits of Weight Standardization in micro-batch training scenarios, where it outperforms other normalization methods. Understand how standardizing weights in convolutional layers smooths the loss landscape by reducing Lipschitz constants. Gain insights into the paper's methodology, results, and implications for various computer vision tasks, including image classification, object detection, and semantic segmentation.