![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F2599569%2F427fc13d625ddce77ebbca34aad5ddd8%2Fgn.png?generation=1600333945567149&alt=media\")
Hello all!
\nThanks to this competition, I stumbled across this amazing read from the Tensorflow 2.3 guide (link pasted at the end).
\nNormally, all of us are aware of Batch Normalization, seen quite frequently when we study Artificial Neural Networks. However, there are other more powerful and task specific normalization techniques such :
\nQuoting from the guide :
\n------------------------------GROUP NORM
\nGroup Normalization(GN) divides the channels of your inputs into smaller sub groups and normalizes these values based on their mean and variance. Since GN works on a single example this technique is batchsize independent.
GN experimentally scored closed to batch normalization in image classification tasks. It can be beneficial to use GN instead of Batch Normalization in case your overall batch_size is low, which would lead to bad performance of batch normalization.
\n--------------------------------INSTANCE NORM
\nInstance Normalization is special case of group normalization where the group size is the same size as the channel size (or the axis size).
Experimental results show that instance normalization performs well on style transfer when replacing batch normalization.
\n
For more details visit :
\nhttps://www.tensorflow.org/addons/tutorials/layers_normalizations#instance_normalization_tutorial
\nHope this helps!!
\nAll the best !~~
Hello all!
\nThanks to this competition, I stumbled across this amazing read from the Tensorflow 2.3 guide (link pasted at the end).
\nNormally, all of us are aware of Batch Normalization, seen quite frequently when we study Artificial Neural Networks. However, there are other more powerful and task specific normalization techniques such :
\nQuoting from the guide :
\n------------------------------GROUP NORM
\nGroup Normalization(GN) divides the channels of your inputs into smaller sub groups and normalizes these values based on their mean and variance. Since GN works on a single example this technique is batchsize independent.
GN experimentally scored closed to batch normalization in image classification tasks. It can be beneficial to use GN instead of Batch Normalization in case your overall batch_size is low, which would lead to bad performance of batch normalization.
\n--------------------------------INSTANCE NORM
\nInstance Normalization is special case of group normalization where the group size is the same size as the channel size (or the axis size).
Experimental results show that instance normalization performs well on style transfer when replacing batch normalization.
\n
For more details visit :
\nhttps://www.tensorflow.org/addons/tutorials/layers_normalizations#instance_normalization_tutorial
\nHope this helps!!
\nAll the best !~~
Thanks, for the link!)
", "rawMarkdown": "Thanks, for the link!)", "votes": null } ], "comments": [ { "id": 1028150, "author_name": "antonantonyan", "author_url": "", "post_date": "09/26/2020 16:28:02", "content": "Thanks, for the link!)
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "1014214": "Hello all!\n\nThanks to this competition, I stumbled across this amazing read from the Tensorflow 2.3 guide (link pasted at the end).\n\nNormally, all of us are aware of Batch Normalization, seen quite frequently when we study Artificial Neural Networks. However, there are other more powerful and task specific normalization techniques such : \n- Group Normalization\n- Instance Normalization\n\nQuoting from the guide : \n------------------------------GROUP NORM\n**Group Normalization(GN) divides the channels of your inputs into smaller sub groups and normalizes these values based on their mean and variance. Since GN works on a single example this technique is batchsize independent**.\n\n**GN experimentally scored closed to batch normalization in image classification tasks. It can be beneficial to use GN instead of Batch Normalization in case your overall batch_size is low, which would lead to bad performance of batch normalization**.\n\n--------------------------------INSTANCE NORM\n**Instance Normalization is special case of group normalization where the group size is the same size as the channel size (or the axis size)**.\n\n**Experimental results show that instance normalization performs well on style transfer when replacing batch normalization**. \n\n\n\nFor more details visit : \n\n**https://www.tensorflow.org/addons/tutorials/layers_normalizations#instance_normalization_tutorial** \n\nHope this helps!!\nAll the best !~~", "1028150": "Thanks, for the link!)" }, "source": "meta" }