Suggested Answer:C🗳️
Post batch normalization statistics (PBN) is the Microsoft Cognitive Toolkit (CNTK) version of how to evaluate the population mean and variance of Batch Normalization which could be used in inference Original Paper. In CNTK, custom networks are defined using the BrainScriptNetworkBuilder and described in the CNTK network description language "BrainScript." Scenario: Local penalty detection models must be written by using BrainScript. Reference: https://docs.microsoft.com/en-us/cognitive-toolkit/post-batch-normalization-statistics
In case study they have also mentioned
- All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too slow
- The images and videos will have varying sizes and formats
So Batch normalization is usefull to speedup the process where as Cosine normalization is usefull to handle varying sizes and formats of input data.
The best normalization type to use in this case is batch normalization. Batch normalization is a technique that reduces the internal covariate shift of the inputs to each layer of a neural network, making the training faster and more stable. Batch normalization also has the benefit of regularizing the model and reducing the need for dropout.
DNN normalization?? I really do not expect this kind of questions ...
The most common one is batch, and weight is kind of a batch with some improvements ...
For other two, I do not know ...
The images and videos will have varying sizes and formats. Normalization mean put them into the same dimension and same format images / videos before further processing
"Local penalty detection models must be written by using BrainScript." BrainScript is used in Microsoft Cognitive Toolkit (CNTK) and it's network definition only supports batch normalization. So C is correct.
https://docs.microsoft.com/en-us/cognitive-toolkit/batchnormalization
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