DEEP LEARNING ARCHITECTURE FOR AUTOMATED IMAGE FEATURE EXTRACTION

摘要

Systems and techniques for training an auto-encoder on a single class are presented. In one example, a system trains an auto-encoder based on first data associated with a first class to generate a trained auto-encoder. The system also applies, using a multiplier, gain data indicative of a gain value to second data associated with the first class and third data associated with a second class to generate enhanced input data that represents a differentiation between the second data associated with the first class and the third data associated with the second class. An input enhancer comprises the trained auto-encoder and the multiplier. Furthermore, the system trains a convolutional neural network based on the enhanced input data to generate a trained convolutional neural network. The system also classifies the first class and the second class based on the input enhancer and the trained convolutional neural network.