Samsung Sds Co., Ltd.

SooAh Cho

JoonHo Lee

JoonSeok Lee

Seongho Joe

Youngjune Gwon

Minyoung Lee

Jieun Song

The present disclosure relates to a document classification method and a document classification device. A document classification method performed by a processor inside a computing device according to an embodiment of the present disclosure may include: obtaining a predicted dimension which is a dimension of a feature vector necessary to classify a document image that has been input, through a learned dimension prediction model; generating a feature vector of the document image, through a feature extraction model based on the predicted dimension; and identifying a document type corresponding to the document image on the basis of the generated feature vector.

Document classification method and document classification device

The present disclosure relates to a text-based document classification method and a document classification device. A text-based document classification method according to an embodiment of the present disclosure is performed by a processor inside a computing device, and may include: extracting, from a document image that has been input, words included in the document image; generating, based on a degree of similarity between the words, a word set including a configured number of words; generating a word set image by individually turning the word set into an image; extracting an important keyword used for document classification among words included in the word set image; and classifying a type of the document image by using the important keyword.

Text-based document classification method and document classification device

An apparatus is for unsupervised domain adaptation for allowing a deep learning model with supervised learning on a source domain completed to be subjected to unsupervised domain adaptation to a target domain. The apparatus includes a first learning unit to perform a forward pass by inputting a pair (x, y) of first data xof the source domain and a label yfor each of the first data and second data xbelonging to the target domain, and insert a dropout following a Bernoulli distribution into the deep learning model in performing the forward pass, and a second learning unit to perform a back propagation to minimize uncertainty about the learning parameter of the deep learning model by using a predicted value for each class output through the forward pass and the label y, and an uncertainty vector for the second data xoutput through the forward pass as inputs.

Apparatus and method for unsupervised domain adaptation

A few-shot learning method according to an embodiment may be performed in a computing device including one or more processors and a memory storing one or more programs executed by the one or more processors. The method may include performing a first task for subjecting a first model to few-shot learning (FSL) based on one or more meta-training data and performing a second task for subjecting a second model to supervised learning based on one or more derived data modified from the one or more meta-training data.

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Seoul, South Korea

SooAh Cho