This document discusses a method called "data fingerprinting" to represent data as signatures that capture the underlying structure and semantics. It presents two case studies applying this method to question complexity analysis and image recognition with limited data. The method uses autoencoders trained on clustered data to extract and encode structural patterns, allowing data-hungry machine learning algorithms to be used for "small-data" applications. Evaluation results demonstrate it can accurately classify new data types not seen during training.
Sequence of systemcalls execution a computer program
Sequence of words a sentence
Organization of pixel intensities in a 2d space image
Sequence of images video
Explain objective : an objective is to detect if a question can be answered by a trained API-based model. Objective can also be to detect if a cell is not deformed.
Explain that this is similar to an auto encoder’s F and INV(F) except that INV can return the representation of any element in S’