“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

IEEE Transactions on Pattern Analysis and Machine Intelligence

A morphable model for the synthesis of 3D faces

2D and 3D face recognition: A survey

As financial time series are inherently noisy and non-stationary, it is regarded as one of the most challenging applications of time series forecasting. Due to the advantages of generalization capability in obtaining a unique solution, support vector regression (SVR) has also been successfully applied in financial time series forecasting. In the modeling of financial time series using SVR, one of the key problems is the inherent high noise. Thus, detecting and removing the noise are important but difficult tasks when building an SVR forecasting model. To alleviate the influence of noise, a two-stage modeling approach using independent component analysis (ICA) and support vector regression is proposed in financial time series forecasting. ICA is a novel statistical signal processing technique that was originally proposed to find the latent source signals from observed mixture signals without having any prior knowledge of the mixing mechanism. The proposed approach first uses ICA to the forecasting variables for generating the independent components (ICs). After identifying and removing the ICs containing the noise, the rest of the ICs are then used to reconstruct the forecasting variables which contain less noise and served as the input variables of the SVR forecasting model. In order to evaluate the performance of the proposed approach, the Nikkei 225 opening index and TAIEX closing index are used as illustrative examples. Experimental results show that the proposed model outperforms the SVR model with non-filtered forecasting variables and a random walk model.

Financial time series forecasting using independent component analysis and support vector regression

An augmented reality image display system may be implemented together with a surgical robot system. The surgical robot system may include a slave system performing a surgical operation, a master system controlling the surgical operation of the slave system, an imaging system generating a virtual image of the inside of a patient's body, and an augmented reality image display system including a camera capturing a real image having a plurality of markers attached to the patient's body or a human body model. The augmented reality image system may include an augmented reality image generator which detects the plurality of markers in the real image, estimates the position and gaze direction of the camera using the detected markers, and generates an augmented reality image by overlaying a region of the virtual image over the real image, and a display which displays the augmented reality image.

Augmented reality image display system and surgical robot system comprising the same

Stimuli-Responsive MXene-Based Actuators

A method and apparatus for recognizing and searching for a face using 2nd-order independent component analysis (ICA) are provided. The method for describing feature points uses 2nd-order ICA d to describe a facial image space and improve recognition performance in various illumination conditions. According to the method and apparatus, use of pose or illumination invariant face descriptor enables retrieval of human faces and authentication of a specific individual.

Method and apparatus of recognizing face using component-based 2nd-order principal component analysis (PCA)/independent component analysis (ICA)

Unsupervised domain adaptation (UDA) methods for learning domain invariant representations have achieved remarkable progress. However, few studies have been conducted on the case of large domain discrepancies between a source and a target domain. In this paper, we propose a UDA method that effectively handles such large domain discrepancies. We introduce a fixed ratio-based mixup to augment multiple intermediate domains between the source and target domain. From the augmented-domains, we train the source-dominant model and the target-dominant model that have complementary characteristics. Using our confidence-based learning methodologies, e.g., bidirectional matching with high-confidence predictions and self-penalization using low-confidence predictions, the models can learn from each other or from its own results. Through our proposed methods, the models gradually transfer domain knowledge from the source to the target domain. Extensive experiments demonstrate the superiority of our proposed method on three public benchmarks: Office-31, Office-Home, and VisDA-2017.

FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation.

A scalable face recognition method and apparatus using complementary features. The scalable face recognition apparatus includes a multi-analysis unit which analyzes a plurality of features of an input face image using a plurality of feature analysis techniques separately, compares the features of the input face image with a plurality of features of a reference image; and provides similarities as the results of the comparison, a fusion unit which fuses the similarities, and a determination unit which classifies the input face image according to a result of the fusion performed by the fusion unit.

Wonjun Hwang

Papers

Augmented reality image display system and surgical robot system comprising the same

Stimuli-Responsive MXene-Based Actuators

Method and apparatus of recognizing face using component-based 2nd-order principal component analysis (PCA)/independent component analysis (ICA)

FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation.

Scalable face recognition method and apparatus based on complementary features of face image