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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. The paper focuses on building convolutional residual style networks without batch normalization, which typically results in lower performance or inability to scale to larger batch sizes. The NF Nets presented in the paper can scale to large batch sizes and are more efficient than previous state-of-the-art methods, such as EfficientNets. They are now considered the new efficient model in image recognition.
