This isn't really a theano question and might get more traction elsewhere.
Have you considered adding a "no action" class to your problem for the
frames/times where nothing is happening?
On Thursday, September 15, 2016 at 8:15:01 AM UTC-7, aditya vora wrote:
> 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.
> With Regards,
> Aditya Vora
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