Novelty detection using deep learning neural network

Abstract

The disclosed technology generally relates to novelty detection and more particularly to novelty detection methods using a deep learning neural network and apparatuses and non-transitory computer-readable media configured for performing the methods. In one aspect, a method for detecting novelty using a deep learning neural network model comprises providing a deep learning neural network model. The deep learning neural network model comprises an encoder comprising a plurality of encoder layers and a decoder comprising a plurality of decoder layers. The method additionally comprises feeding a first input into the encoder and successively processing the first input through the plurality of encoder layers to generate a first encoded input, wherein successively processing the first input comprises generating a first intermediate encoded input from one of the encoder layers prior to generating the first encoded input. The method additionally comprises feeding the first encoded input from the encoder into the decoder and successively processing the first encoded input through the plurality of decoder layers to generate a first reconstructed output. The method additionally comprises feeding the first reconstructed output from the decoder as a second or subsequent input into the encoder and successively processing the first reconstructed output through the plurality of encoder layers, wherein successively processing the first reconstructed output comprises generating a second intermediate encoded input from the one of the encoder layers. The method further comprises detecting a novelty of the original input based on a comparison of the first intermediate encoded input and the second intermediate encoded input.