38 results about "Synthetic data generation" patented technology

A control apparatus that improves the usability of a vehicle surrounding monitoring apparatus without confusing the monitoring party while monitoring the surroundings of a vehicle. A detection area setting section (2) sets a detection area subject to obstacle detection, in the photographing range of an imaging section (1) installed in the vehicle. An obstacle detection section (4) detects an obstacle from the detection area set in an image photographed by the imaging section (1). A synthesized data generating section (5) performs image processing such that information for distinguishing between the inside and outside of the set detection area is displayed superimposed on the image photographed by the imaging section (1).

This application relates generally to augmenting images and videos with dynamic object compositing, and more specifically, to generating synthetic training data to train a machine learning model to automatically augment an image or video with a dynamic object. The synthetic training data may contain multiple data points from thousands of simulated dynamic object movements within a virtual environment. Based on the synthetic training data, the machine learning model may determine the movement of a new dynamic object within new virtual environment.

A method of generating synthetic data from time series data, such as from handwritten characters, words, sentences, mathematics, and sketches that are drawn with a stylus on an interactive display or with a finger on a touch device. This computationally efficient method is able to generate realistic variations of a given sample. In a handwriting or sketch recognition context, synthetic data is generated from real data in order to train recognizers and thus improve recognition accuracy when only a limited number of samples are available. Similarly, synthetic data can also be used to test and validate such recognizers. Also discussed is a dynamic time warping based approach for both segmented and continuous data that is designed to be a robust, go-to method for gesture recognition across a variety of modalities using only limited training samples.

Embodiments of the present invention provide an improvement to convention machine model training techniques by providing an innovative system, method and computer program product for the generation of synthetic data using an iterative process that incorporates multiple machine learning models and neural network approaches. A collaborative system for receiving data and continuously analyzing the data to determine emerging patterns is provided. Common characteristics of data from the identified emerging patterns are broadened in scope and used to generate a synthetic data set using a generative neural network approach. The resulting synthetic data set is narrowed based on analysis of the synthetic data as compared to the detected emerging patterns, and can then be used to further train one or more machine learning models for further pattern detection.

A technique for drawing or managing multimedia data by desired groups. In a built-in memory of a cellular phone terminal, thirteen picked-up image data are stored. In tag information of each of the thirteen picked-up image data, information on date and time is recorded when an image of the data is picked up. When a user specifies the range of image pickup date and time, eight picked-up image data that match the specified range of image pickup date and time are selected and synthetic image data is generated from these eight picked-up image data.

This application relates generally to augmenting images and videos with dynamic object compositing, and more specifically, to generating synthetic training data to train a machine learning model to automatically augment an image or video with a dynamic object. The synthetic training data may contain multiple data points from thousands of simulated dynamic object movements within a virtual environment. Based on the synthetic training data, the machine learning model may determine the movement of a new dynamic object within new virtual environment.

The present invention relates to an optical tomographic image generating method including: obtaining signals for a plurality of frames; obtaining respective complex number data by performing Fourier transformation of the signals for the plurality of frames; synthesizing the plurality of frames using the respective complex number data; generating a tomographic image based on the synthesized data. This configuration enables easy enhancement of the image quality in an optical coherence tomographic imaging apparatus.

The invention relates to a motor vehicle license plate synthetic data generation method, which comprises the following steps: firstly, obtaining a license plate font from an official public specification file and modeling to enable the synthetic license plate data to be close to the condition of a real license plate; secondly, utilizing a virtual engine to construct a scene, carrying out omnibearing rendering, generating annotations automatically, and improving the collection and generation efficiency of a data set; thirdly, setting diversified license plate generation conditions in the aspects of weather, illumination, distance, angle and the like, and obtaining data with more comprehensive coverage; and finally, performing style migration on the generated synthetic license plate data byusing a style migration technology, so that the difference between an artificially synthesized image and a real image domain is reduced.

An exemplary system, method, and computer-accessible medium can include, for example, (a) receiving a dataset(s), (b) determining if a misclassification(s) is generated during a training of a model(s) on the dataset(s), (c) generating a synthetic dataset(s) based on the misclassification(s), and (d) determining if the misclassification(s) is generated during the training of the model(s) on the synthetic dataset(s). The dataset(s) can include a plurality of data types. The misclassification(s) can be determined by determining if one of the data types is misclassified. The dataset(s) can include an identification of each of the data types in the dataset(s).

Embodiments described herein provide a system for facilitating a training system for a device. During operation, the system determines a system model for the device that can be based on empirical data of the device. The empirical data is obtained based on experiments performed on the device. The system then generates, from the system model, synthetic data that represents behavior of the device under a failure. The system determines uncertainty associated with the synthetic data and, from the uncertainty, determines a set of prediction parameters using an uncertainty quantification model. The system generates training data from the synthetic data based on the set of prediction parameters and learns a set of learned parameters associated with the device by using a machine-learning-based classifier on the training data.

Systems and methods to generate artificial intelligence models with synthetic data are disclosed. An example system includes a deep neural network (DNN) generator to generate a first DNN model using first real data. The example system includes a synthetic data generator to generate first synthetic data from the first real data, the first synthetic data to be used by the DNN generator to generate a second DNN model. The example system includes an evaluator to evaluate performance of the first and second DNN models to determine whether to generate second synthetic data. The example system includes a synthetic data aggregator to aggregate third synthetic data and fourth synthetic data from a plurality of sites to form a synthetic data set. The example system includes an artificial intelligence model deployment processor to deploy an artificial intelligence model trained and tested using the synthetic data set.

Techniques for synthetic data generation in computer-based reasoning systems are discussed and include receiving a request for generation of synthetic data based on a set of training data cases. One or more focal training data cases are determined. For undetermined features (either all of them or those that are not subject to conditions), a value for the feature is determined based on the focal cases. In some embodiments, the generated synthetic data may be checked for similarity against the training data, and if similarity conditions are met, it may be modified (e.g., resampled), removed, and / or replaced.

A method for generating synthetic data records which include datasets that capture state-based transitions, according to which a state transition family is randomly selecting, according to the distribution of samples between the different clusters of users and the context variables are randomly sampled according to their distribution within the chosen cluster. The relevant Markov Chains models are selected according to the sampled context and the initial state of the sequence is randomly selected according to the distribution of states. A random walk process is initialized on the graph models and the random walk is performed process on each context separately, assuming context independency. The cause condition of the current transition is sampled for each state transition, based on the distributions on the selected edge.

Techniques for synthetic data generation in computer-based reasoning systems are discussed and include receiving a request for generation of synthetic training data based on a set of training data cases. One or more focal training data cases are determined. For undetermined features (either all of them or those that are not subject to conditions), a distribution for the feature among the training cases is determined, and a value for the feature is determined based on that distribution. In some embodiments, the distribution may be perturbed based on target surprisal. In some embodiments, generated synthetic data may be tested for fitness. Further, the generated synthetic data may be provided in response to a request, used to train a computer-based reasoning model, and / or used to cause control of a system.

An ultrasound image displaying apparatus includes: a composite data generating unit which generates composite data in which data based on echo signals obtained by transmitting ultrasound to a subject are multiple-frame combined; a display unit on which an ultrasound image based on the composite data is displayed; and an index calculating unit which calculates an index related to the amount of motion artifacts in the ultrasound image for each frame, based on the data. The composite data generating unit generates composite data combined using data of some or all frames in each of which the index satisfies a prescribed reference.

The invention discloses an electric power line patrol electric tower detection and identification method and system based on an unmanned aerial vehicle, which are suitable for a pre-trained tower identification neural network model, and the method comprise the following steps: controlling the unmanned aerial vehicle to perform automatic patrol according to a visual positioning method; collecting an image in the unmanned aerial vehicle inspection process, generating synthetic data according to the image, and transmitting the synthetic data to the trained tower identification neural network model; and the tower identification neural network model generates belief mapping based on the synthetic data, identifies the tower according to the belief mapping, and outputs a final identification result. The tower in the image acquired in the unmanned aerial vehicle inspection process is automatically identified through the tower identification neural network model, the unmanned aerial vehicle iscontrolled to perform automatic inspection through the visual positioning method, human intervention is not needed in the whole process, the detection precision is high, the labor cost is saved, and the working efficiency is greatly improved.

Techniques for synthetic data generation in computer-based reasoning systems are discussed and include receiving a request for generation of synthetic data based on a set of training data cases. One or more focal training data cases are determined. For undetermined features (either all of them or those that are not subject to conditions), a value for the feature is determined based on the focal cases. In some embodiments, the generated synthetic data may be checked for similarity against the original data, and if similarity conditions are met, it may be modified (e.g., resampled), removed, and / or replaced.

A system and method are disclosed for training a system or a model using any combination of synthetic data and real data. The various aspects of the invention include generation of data that is used to supplement or augment real data, wherein the subject of the data can be segmented. Labels or attributes are automatically added to generated data. The generated data is synthetic data that is generated using seed or real data as well as from other synthetic data. Using the synthetic data, various domain adaptation models can be used and trained.

A system and method are disclosed for training a model using a training dataset. The training dataset can be made up of only real data, only synthetic data, or any combination of synthetic data and real data. The images ae segmented to define objects with known labels. The object is pasted onto backgrounds to generated synthetic datasets. The various aspects of the invention include generation of data that is used to supplement or augment real data. Labels or attributes can be automatically added to the data as it is generated. The data can be generated using seed data. The data can be generated using synthetic data. The data can be generated from any source, including the user's thoughts or memory. Using the training dataset, various domain adaptation models can be trained.

Techniques for synthetic data generation in computer-based reasoning systems are discussed and include receiving a request for generation of synthetic data based on a set of training data cases. One or more focal training data cases are determined. For undetermined features (either all of them or those that are not subject to conditions), a value for the feature is determined based on the focal cases. In some embodiments, the generated synthetic data may be checked for similarity against the training data, and if similarity conditions are met, it may be modified (e.g., resampled), removed, and / or replaced.

An exemplary system, method, and computer-accessible medium can include, for example, (a) receiving a dataset(s), (b) determining if a misclassification(s) is generated during a training of a model(s) on the dataset(s), (c) generating a synthetic dataset(s) based on the misclassification(s), and (d) determining if the misclassification(s) is generated during the training of the model(s) on the synthetic dataset(s). The dataset(s) can include a plurality of data types. The misclassification(s) can be determined by determining if one of the data types is misclassified. The dataset(s) can include an identification of each of the data types in the dataset(s).

Techniques for synthetic data generation in computer-based reasoning systems are discussed and include receiving a request for generation of synthetic training data based on a set of training data cases. One or more focal training data cases are determined. For undetermined features (either all of them or those that are not subject to conditions), a value for the feature is determined based on the focal cases. In some embodiments, validity of the generated value may be checked based on feature information. In some embodiments, generated synthetic data may be checked against all or a portion of the training data to ensure that it is not overly similar.

Techniques for synthetic data generation in computer-based reasoning systems are discussed and include receiving a request for generation of synthetic data based on a set of training data cases. One or more focal training data cases are determined. For undetermined features (either all of them or those that are not subject to conditions), a value for the feature is determined based on the focal cases. In some embodiments, the generation of synthetic data may be conditioned on values of features, preserved features, such as unique identifiers, previous-in-time features, and using the other techniques discussed herein.

It is possible to provide a technique for plotting or managing multimedia data batched into a desired group. A built-in memory (17) of a mobile telephone terminal (1) contains thirteen captured image data (A1 to A13). Tag information attached to the captured image data (A1 to A13) includes date and time when respective image data were captured. When a user specifies an imaging date and time range, eight captured image data (A4 to A11) matched with the specified imaging date and time range are selected and synthesis image data (22) is generated from the eight captured image data.

A method of generating synthetic data from time series data, such as from handwritten characters, words, sentences, mathematics, and sketches that are drawn with a stylus on an interactive display or with a finger on a touch device. This computationally efficient method is able to generate realistic variations of a given sample. In a handwriting or sketch recognition context, synthetic data is generated from real data in order to train recognizers and thus improve recognition accuracy when only a limited number of samples are available. Similarly, synthetic data can also be used to test and validate such recognizers. Also discussed is a dynamic time warping based approach for both segmented and continuous data that is designed to be a robust, go-to method for gesture recognition across a variety of modalities using only limited training samples.

A system and method are disclosed for training a model using a training dataset. The training dataset can include only real data, only synthetic data, or any combination of synthetic data and real data. The various aspects of the invention include generation of data that is used to supplement or augment real data. Labels or attributes can be automatically added to the data as it is generated. The data can be generated using seed data. The data can be generated using synthetic data. The data can be generated from any source, including the user's thoughts or memory. Using the training dataset, various domain adaptation models can be trained.

A synthetic data generation apparatus according to an embodiment includes a generator for generating synthetic data from an input value, a first discriminator learned to distinguish between actual data and the synthetic data, a second discriminator learned to distinguish between the actual data and the synthetic data while satisfying differential privacy, and a third discriminator learned to distinguish between first synthetic data which is output from the generator learned by the first discriminator and second synthetic data which is output from the generator learned by the second discriminator.

An apparatus for generating synthetic data according to an embodiment includes a synthetic data generator configured to generate synthetic data corresponding to combined data obtained by combining original data held by each of a plurality of data providing apparatuses, and a synthetic data provider configured to provide the synthetic data to a data using apparatus.