Abstract: 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.

Abstract: 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.

Abstract: 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 (xsi, ysi) of first data xsi of the source domain and a label ysi for each of the first data and second data xTj belonging 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 ysi, and an uncertainty vector for the second data xTj output through the forward pass as inputs.

Abstract: 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.

Abstract: 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.

Abstract: 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 (xsi, ysi) of first data xsi of the source domain and a label ysi for each of the first data and second data xTj belonging 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 ysi, and an uncertainty vector for the second data xTj output through the forward pass as inputs.

Abstract: A glaucoma diagnosis apparatus according to an embodiment includes a fundus image processor configured to receive a fundus image and extract a first region of interest (ROI) and a second ROI from the received fundus image, an image classification neural network configured to learn the extracted first ROI and perform classification into a normal fundus image and a glaucoma fundus image on the basis of the learned first ROI, a vertical cup-to-disc ratio (vCDR) calculator configured to recognize an optic disc (OD) and an optic cup (OC) from the extracted second ROI and calculate a vCDR, and a determinator configured to aggregate a vCDR calculation result and an image classification result of the image classification neural network to determine whether glaucoma is present in the fundus image.