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The new family and approach of BatchNorm-free NN architectures look very perspective due to the lack of BatchNorm training support.
In the paper "High-Performance Large-Scale Image Recognition Without Normalization" the new approach is proposed.
We should apply this approach to the traditional ResNet architecture to prevent gradient exploding without BatchNorm layers.
The pre-trained weights will be a plus but are not nessesary.
The text was updated successfully, but these errors were encountered:
How to obtain pre-trained weights https://reposhub.com/python/deep-learning/benjs-nfnets_pytorch.html
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An example of pytorch implementation https://nfnets-pytorch.readthedocs.io/en/latest/
zaleslaw
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The new family and approach of BatchNorm-free NN architectures look very perspective due to the lack of BatchNorm training support.
In the paper "High-Performance Large-Scale Image Recognition Without Normalization" the new approach is proposed.
We should apply this approach to the traditional ResNet architecture to prevent gradient exploding without BatchNorm layers.
The pre-trained weights will be a plus but are not nessesary.
The text was updated successfully, but these errors were encountered: