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Image classification without normalization that is faster and better than with normalization

Image classification without normalization that is faster and better than with normalization


High-Performance Large-Scale Image Recognition Without Normalization

arXiv paper abstract https://arxiv.org/abs/2102.06171



Batch normalization is a key component of most image classification models, but it has many undesirable properties stemming from its dependence on the batch size and interactions between examples.


... a significantly improved class of Normalizer-Free ResNets.


... smaller models match the test accuracy of an EfficientNet-B7 on ImageNet while being up to 8.7x faster to train, and


... largest models attain a new state-of-the-art top-1 accuracy of 86.5%.


... significantly better performance than their batch-normalized counterparts when finetuning on ImageNet after large-scale pre-training on a dataset of 300 million labeled images, with our best models obtaining an accuracy of 89.2%. ...



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