Hello , We are training a 3D convolutional neural network with real life videos that we recorded from cameras. Actually, my dataset has around 3000 video clips which are obtained by dividing a single surveillance video. Each clip was appropriately labelled by us. I need to classify this videos into different actions. However, as the data is obtained by capturing real life videos it is certainly not possible to have totally clean data, as a result, there are certain situations in the video clips in which there are no actions happening. However we tried to remove such clips as much as possible but still couldn't filter out all clips because some frames of the same clip had actions in it (Eg. Person running and going out of the frame). So my question is how the convolutional neural network training shall behave in such a situation? Will the network be immune to such "outliers" as most of the data is appropriate? If it will affect the training then how it shall affect and if not how? I need a mathematical proof to this question in terms of parameter update and stuff.
Thanks, With Regards, Aditya Vora -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
