Group Normalization
arxiv.org
Group Normalization
1–10 of 13 posts
Re: Group Normalization
#2No relation of subgroup normalisation, just a bad name.
Re: Group Normalization
#3Has anyone made or found an implementation yet?
Re: Group Normalization
#4Has anyone made or found an implementation yet?
Figure 3 in the paper.
Re: Group Normalization
#5Has anyone made or found an implementation yet?
available in PyTorch master via: https://github.com/pytorch/pytorch/pull/5968
Re: Group Normalization
#6Mostly only relevant for convnets it seems
Re: Group Normalization
#7Has anyone made or found an implementation yet?
When it comes to using super new research models or methods in deep learning, implementing it yourself instead of looking for other implementations is almost a requirement. In my experience the majority of online "implementations" of research deep learning models or methods have subtle bugs, flaws, or outright implemented something different than the paper they reference. This isn't limited to random github code: for example, I wouldn't trust anything in tensorflow.contrib unless I read the source.
Re: Group Normalization
#8Has anyone made or found an implementation yet?
When it comes to using super new research models or methods in deep learning, implementing it yourself instead of looking for other implementations is almost a requirement. In my experience the majority of online "implementations" of research deep learning models or methods have subtle bugs, flaws, or outright implemented something different than the paper they reference. This isn't limited to random github code: for…
Completely agree. Well, tf.contrib I am usually happy with the docs and a glance through GitHub issues, but, yes, code found elsewhere for very new, or even just recent papers can be really hit or miss, and not always obviously so. There is a lot of great stuff out there, but sometimes even popular papers over a year old can still be a challenge to find good code for. finding 3 separate implementations, all stlighty wrong or unusable is not uncommon.
Re: Group Normalization
#9Has anyone made or found an implementation yet?
Re: Group Normalization
#10Has anyone made or found an implementation yet?
When it comes to using super new research models or methods in deep learning, implementing it yourself instead of looking for other implementations is almost a requirement. In my experience the majority of online "implementations" of research deep learning models or methods have subtle bugs, flaws, or outright implemented something different than the paper they reference. This isn't limited to random github code: for…
Having implemented algorithms from research papers I came to the conclusion that the same is true about the papers. It’s unbelievable how bad the quality of the description of algorithms often is.