Htc Corporation

Edward Chang

Chun-Nan Chou

Kai-Fu Tang

Che-Han Chang

Hao-Cheng Kao

Liang-Kang Huang

Te-Chuan Liu

Chih-Yang Chen

Tung-Peng Wu

Chung-Huan Mei

Meng-Hsi Wu

Chun-Yen Chen

Chun-Hsiang Huang

Chi-Pang Chiang

Tzu-Hao Kuo

Zih-Ci Lin

Ting-Wei Lin

Yu-Ting Lee

Chun-Hsien Yu

Fu-Chieh Chang

Jui-Lin Wu

Yu-Shao Peng

Hsuan-Tien Lin

Cheng-Lung Sung

Yu-Hsun Lin

Szu-Ying Chen

Shang-Xuan Zou

Chuen-Kai Shie

Chung-Hsiang Chuang

Kuan-Chieh Tung

Chia-Chin Tsao

Feng-Yi Chu

Yi-Yu Chung

Yang-En Chen

A machine learning method includes steps of: (a) obtaining initial values of hyperparameters and hypernetwork parameters; (b) generating first classification model parameters according to the hyperparameters and the hypernetwork parameters, and updating the hypernetwork parameters according to a classification result based on the first classification model parameters relative to a training sample; (c) generating second classification model parameters according to the hyperparameters and the updated hypernetwork parameters, and updating the hyperparameters according to another classification result based on the second classification model parameters relative to a verification sample; and (d) repeating the steps (b) and (c) for updating the hypernetwork parameters and the hyperparameters.

Machine learning method and machine learning system involving data augmentation

A control method for a reinforcement learning system includes following operations. The reinforcement learning system obtains training data relating to an interaction system. The interaction system interacts with a reinforcement learning agent. A neural network model is utilized by the reinforcement learning agent for selecting sequential actions from a set of candidate actions. The neural network model is trained to maximize cumulative rewards collected by the reinforcement learning agent in response to the sequential actions. During training of the neural network model, auxiliary rewards of the cumulative rewards are provided to the reinforcement learning agent according to a comparison between symptom inquiry actions of the sequential actions and diagnosed symptoms in the training data.

Control method and reinforcement learning for medical system

A computer aided medical method include following steps. An initial symptom is collected through an interaction interface. A representative prediction model is selected from plural candidate prediction models according to the initial symptom. The candidate prediction models are trained by a machine learning algorithm according to clinical data. A series of sequential actions is generated according to the representative prediction model and the initial symptom. The sequential actions are selected from plural candidate actions in the representative prediction model. The candidate actions include plural inquiry actions and plural disease prediction actions. Each of the sequential actions is one of the inquiry actions or the disease prediction actions. The series of sequential actions is displayed on the interaction interface.

Computer aided medical method and medical system for medical prediction

A method for adjusting a convolutional neural network includes following operations. The convolutional neural network includes convolution layers in a sequential order. Receptive field widths of the convolution layers in a first model of the convolutional neural network are determined. Channel widths of the convolution layers in the first model are reduced into reduced channel widths according to the receptive field widths of the convolution layers and an input image width. A structure of a second model of the convolutional neural network is formed according to the reduced channel widths. The second model of the convolutional neural network is trained according the structure of the second model.

Method and electronic apparatus for adjusting a neural network

The disclosure provides a method for training generative adversarial network (GAN), a method for generating images by using GAN, and a computer readable storage medium. The method may train the first generator of the GAN with available training samples belonging to the first type category and share the knowledge learnt by the first generator to the second generator. Accordingly, the second generator may learn to generate (fake) images belonging to the second type category even if there are no available training data during training the second generator.

Method for training generative adversarial network (GAN), method for generating images by using GAN, and computer readable storage medium

A computer aided medical method includes the following steps. An initial symptom of a patient and context information is collected through an interaction interface. Actions in a series are sequentially generated according to the candidate prediction models and the initial symptom. Each of the actions corresponds to one of the inquiry actions or one of the disease prediction actions. If the latest one of the sequential actions corresponds to one of the disease prediction actions, potential disease predictions are generated in a first ranking evaluated by the candidate prediction models. The first ranking is adjusted into a second ranking according to the context information. A result prediction corresponding to the potential disease predictions is generated in the second ranking.

A training task optimization system includes a processor. The processor is configured to receive training environment information of a training task. The training environment information at least carries information corresponding to training samples in the training task. The processor is configured to calculate a memory distribution for the training task based on memory factors, the training samples and a neural network, and select a mini-batch size that is fit to the memory distribution. In response to the training environment information, the processor is configured to output the mini-batch size for execution of the training task.

Training task optimization system, training task optimization method and non-transitory computer readable medium for operating the same

An image processing training method includes the following steps. A template label image is obtained, in which the template label image comprises a label corresponding to a target. A plurality of first reference images are obtained, in which each of the first reference images comprises object image data corresponding to the target. A target image according to the template label image and the first reference images is generated, in which the target image comprises a generated object, a contour of the generated object is generated according to the template label image, and a color or a texture of the target image is generated according to the first reference images.

Image processing method, non-transitory computer readable storage medium and image processing system

A method includes acquiring a plurality of training images through a capturing component, acquiring a plurality of training camera poses of the capturing component corresponding to the training images through a pose sensor disposed corresponding to the capturing component, and training a camera pose estimation model according to the training images and the training camera poses of the capturing component.

Method and device for camera pose estimation

An image processing method includes generating, by a processing component, a first input feature map based on an input image using a first convolutional neural network; generating, by the processing component, a first template feature map based on a template image using the first convolutional neural network; generating, by the processing component, a first estimated motion parameter based on an initial motion parameter, the first input feature map and the first template feature map using an iterative Lucas-Kanade network; and performing, by the processing component, image alignment between the input image and the template image based on the first estimated motion parameter.

Method, device, and non-transitory computer readable storage medium for image processing

An electronic apparatus includes a displayer, a graphic processing circuit, sensors and a control circuit. The displayer is configured for displaying a virtual reality scene or an augmented reality scene. The graphic processing circuit is coupled to the displayer. The graphic processing circuit is configured for computing a plurality of scene segments in the virtual reality scene or the augmented reality scene. The sensors are configured for collecting attention factors. The control circuit is coupled to the sensors and the graphic processing circuit. The control circuit is adapted to generate an attention map according to the attention factors. The attention map indicates priority rankings of the scene segments. The control circuit is further adapted to allocate a distribution of computational resources of the graphic processing circuit between the scene segments according to the priority rankings. The scene segments are computed differentially according to the distribution of computational resources.

Electronic apparatus and method for virtual reality or augmented reality system

The present disclosure provides a method, an electronic device, and a user interface for searching a plurality of relevant via active searching. At first, a query image having a first subject and a second subject may be obtained from stored images. Next, a query concept based on content information of the query image may be learned by sampling first sample images from the stored images according to content information of the query image for relevance feedback. Then, the query concept is refined based on context information of the first selected images among the first sample images. Based on the refined query concept, relevant images among the stored images may be searched according to the query concept and grouped into a collection album.

Method for searching relevant images via active learning, electronic device using the same

An image capturing method, a panorama image generating method and an electronic apparatus are provided in this disclosure. The image capturing method includes steps of: calculating feature points within a first image shot by the electronic apparatus; calculating a maximal rotation angle of the electronic apparatus rotating along a direction according to a distribution of the feature points within the first image; and, shooting a second image automatically when the electronic apparatus has been rotated through the maximal rotation angle along the direction.

Growing community of inventors

Taoyuan, Taiwan

Edward Chang