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Welcome to the caffe-mt wiki!

Features of this version of caffe includes:

1.MultiTaskDataLayer:

This layer supports parsing label for multi-task training(classification, regression or any of their combination) without the bother of using mutiple DataLayer instead. (Parallel training not suppoerted for this layer yet)

2.ChannlWiseBNLayer/EltWiseBNLayer

a. ChannlWiseBNLayer supports performing Batch Normalization per channel of its input feature maps, which namely will compute a mean/variance/scale/shift scalar per channel of the input feature maps.

b. EltWiseBNLayer supports performing Batch Normalization per element of the channels of its input feature maps, which namely will compute a mean/variance/scale/shift tensor the same shape as that of the input feature maps.

3.Local Convolution Layer

This layer splits input feature maps into N*N grid(even with overlap) and performs convolution on each piece of the grid using different kernels, and then combine their results into a larger feature map. A related example on StackOverflow is here.

4.Dynamic Convolution Layer

Dynamic convolution means performing convolution using kernels from bottom. Details can be found here and here.