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The video tutorial discusses high performance large-scale image recognition without normalization by Andrew Brock, Silham Day, Samuel L. Smith, and Karen Simonian of DeepMind, also known as NF-Nets. This paper focuses on building convolutional residual style networks without batch normalization, which typically results in lower performance and scalability issues. However, the networks developed in this paper can scale to larger batch sizes and are more efficient than previous state-of-the-art methods. Comparing them to models like EfficientNet, NF-Nets are now considered more efficient and scalable for image recognition tasks.
