3D convolutions and jitter I want to process a sequence of cropped and aligned face images from a video with a neural network. I am considering a use of 3D convolutions in order to capture the spatiotemporal dependency. However, despite alignment and cropping steps, there is still some jitter noise present in the sequence. I am wondering if it is going to be too difficult for 3D convs to finds useful patterns in such a sequence or maybe it will deal with the noise just fine?

You are asking if a convolutional neural network would work with images that are distorted and contain noise. I'm afraid that the answer is "it depends". In some cases, with some kinds of distortions and noise, they will work, in some not. The only way to figure it out is to verify it empirically. If the data is noisy, you may need more data than in a non-noisy case for it to work. It may be worth spending some time on preprocessing to de-noise it. Another possible approach would be to use data augmentation to create more data, where you would use noise-free data to generate artificial samples with different kinds and degrees of noise so that your model has more data to learn to figure out signal from noise.
