Samsung Sds Co., Ltd.

Lg Electronics Inc.

Seung Jai Min

Young June Gwon

Hyun Jae Lee

Hyun Jin Choi

Sung Jun Kim

Jae Woong Yun

Seong Ok Bae

Yeong Dae Kwon

Jin Ho Choo

Ki Hyo Moon

Hyun Bin Loh

Byoung Jip Kim

Jin Yeop Chang

Bong Kyu Hwang

Seong Ho Joe

Jin Hwan Han

Ji Su Jeong

Ju Dong Kim

An apparatus for embedding a sentence feature vector according to an embodiment includes a sentence acquisitor configured to acquire a first sentence and a second sentence, each including one or more words; a vector extractor configured to extract a first feature vector corresponding to the first sentence and a second feature vector corresponding to the second sentence by independently inputting each of the first sentence and the second sentence into a feature extraction network; and a vector compressor configured to compress the first feature vector and the second feature vector into a first compressed vector and a second compressed vector, respectively, by independently inputting each of the first feature vector and the second feature vector into a convolutional neural network (CNN)-based vector compression network.

Apparatus and method for embedding sentence feature vector

A method for training an image classification model according to an embodiment includes training a feature extractor and a rotation angle classifier to predict a rotation angle of each of unlabeled first training images, training the image classification model to predict a label and rotation angle of each of labeled second training images, but predict a uniform label even though an actual rotation angle of each of the second training images is changed, generating a pseudo label based on a training image that satisfy a preset condition among unlabeled candidate images, and training the image classification model to predict a rotation angle of each of the third training images, and predict a label of each of the third training images based on the pseudo label, but predict a uniform label even though an actual rotation angle of each of the third training images is changed.

Method for training image classification model and apparatus for executing the same

A hyperparameter optimization method performed by a hyperparameter optimization apparatus to optimize hyperparameters of a model includes calculating an evaluation score for each of a plurality of hyperparameter samples constituting a first hyperparameter sample set by applying each of the hyperparameter samples to a target model, performing weighted clustering of the hyperparameter samples by using the calculated evaluation scores as weights and constructing a second hyperparameter sample set based on the clustering result.

Hyperparameter optimization method and apparatus

An apparatus for a fast inverse discrete cosine transform (IDCT) by processing only non-zero IDCT input data including non-zero feeding unit connected to each row-column decomposed one dimensional IDCT input for feeding only non-zero input data during each cycle. The first 1-D IDCT and the second 1-D IDCT have the same structure, and 1-D IDCT core with a non-zero feeding unit at its front end. The 1-D IDCT core of the present invention is for performing IDCT operation based on the input scaling method, and the operation of the IDCT core consists of a cosine kernel selection operation using a received non-zero input, multiplication-and-addition, and a storing operation. The non-zero feeding unit for the first 1-D IDCT and the second one are different in their configuration. There are two different methods for implementing the first 1-D IDCT, a stored input feeding method and a zigzag scanned input feeding method. The zigzag scanned input method enables the first 1-D IDCT to get rid of its input memory for storing the IDCT input data.

Apparatus for implementing inverse discrete cosine transform in digital image processing system

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Seoul, South Korea

Seung Jai Min