It is often desirable for these representations to be interpretable in the sense that wecaneasily understand howindividual parts contributetosolving thetask athand.

Methods differ in the underlying architecture and mechanisms they use to support adaptation.

Intuitively, there is a tradeoff between the amount of weakly-labeled dataandtheprecision oftheweaklabels.

Not only for static images, in the filed of video, by adding temporal information into the training and fine-tuning process.

This is a result of improving the trainable parameter requirement from O(N4) to O(m), where N is pixel size and m is numberoffibremodes.

Isitpossibleto acquire therequired domain expertise, without involving human experts?

While capable of generating photo-realistic images, these models often produce unrealistic samples which fall outside of the data manifold.