Htc Corporation

Che-Han Chang

Edward Chang

Chih-Yang Chen

Chun-Hsien Yu

Chun-Nan Chou

Hao-Cheng Kao

Edzer Lienson Wu

Szu-Ying Chen

Shan-Yi Yu

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

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

The disclosure provides a method for verifying training data, a training system, and a computer program produce. The method includes: receiving a labelled result with a plurality of bounding regions, wherein the labelled result corresponds to an image, the bounding regions are labelled by a plurality of annotators, the annotators comprises a first annotator and a second annotator, and the bounding region comprises a first bounding region labelled by the first annotator and a second bounding region labelled by the second annotator; and determining the first bounding region and the second bounding region respectively corresponds to different two target objects or corresponds to one target object according to a similarity between the first bounding region and the second bounding region.

Method for verifying training data, training system, and computer readable medium

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.

Growing community of inventors

Taoyuan, Taiwan

Che-Han Chang