Makinarocks Co., Ltd.

Jongseob Jeon

Sangwoo Shim

Ki Hyun Kim

Byungchan Kim

Andre S Yoon

Yongsub Lim

JeongWoo Choi

Jongsun Shinn

Young Jae Choung

Kyounghyun Mo

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.

Novelty detection using deep learning neural network

An anomaly data detecting method performed by a computing device having at least one processor includes acquiring first time-series data, dividing the first time-series data into a plurality of sub time-series data, adjusting scales of variable values included in at least one sub time-series data among the plurality of sub time-series data and determining whether the first time-series data is abnormal by inputting scaled first time-series data to a neural network based detection model.

Method and apparatus for detecting anomaly data

A method for detecting novelty using an encoder and a decoder comprises: feeding a first input into the encoder and processing the first input through a plurality of encoder layers to generate a first encoded input, wherein processing the first input comprises generating a first intermediate encoded input prior to generating the first encoded input, feeding the first encoded input from the encoder into the decoder and processing the first encoded input through a plurality of decoder layers to generate a first reconstructed output, feeding the first reconstructed output from the decoder as a second or subsequent input into the encoder and processing the first reconstructed output through the plurality of encoder layers, wherein processing the first reconstructed output comprises generating a second intermediate encoded input from the one of the encoder layers, and detecting a novelty based on the first intermediate encoded input and the second intermediate encoded input.

Growing community of inventors

Gyeonggi-do, South Korea

Jongseob Jeon