6 results about "Unlabelled data" patented technology

One aspect of the invention provides a computer-based method for providing corrective feedback about exercise form, the method comprising; recording a user performing a specific exercise: evaluating, by the computer, with machine learning, computer vision, or deep learning models that have been previously trained in order to evaluate the form of a user by training on labelled and or unlabeled datasets that consist of: both correct and incorrect exercise form for the different types of exercises being evaluated; identifying the user throughout the video, the exercise type, each repetition of the exercise, the user's errors in form; and then generating, by the computer, corrective feedback for the user on how to improve exercise form for subsequent repetitions; and communicating, via an output device, the corrective feedback to the user.

A radar target detection method and device based on cross-view cross-modal contrast learning and a medium are provided.The method constructs a self-supervised learning framework for radar target detection based on a distance-azimuth-Doppler (RAD) tensor, captures the relationship between different views of the RAD cube through cross-view contrast learning, enables the model to obtain spatial distribution knowledge from multiple views, and captures the relationship between the RAD and a visual modality through cross-modal contrast learning, enabling the model to obtain semantic knowledge from the visual modality.The present application combines cross-view and cross-modal contrast learning, enhances the feature representation capability of the radar, allows the RAD model to learn transferable feature representations from unlabelled data, and significantly improves the accuracy of radar target detection downstream tasks in the case of limited labelled data.

Broadly speaking, the present techniques provide an automatic way of classifying data items within an environment (e.g. a business, workplace, organisation, etc.), and applying data management policies based on the classifications. This is advantageous over existing techniques which require manual classification of data items, which is time consuming in environments where hundreds of new data items may be generated in a day or week. The present techniques use an embedding machine learning, ML, model to automatically determine the relevant classification label(s) for an unlabelled data item, which is then used to select and apply the relevant data management policy(ies).

The application discloses a kind of data and physical alternation driven semi-supervised seismic wave impedance inversion method, belong to petroleum geophysical exploration field.The method includes: obtaining the poststack seismic record of research area and well impedance curve, constructs label data set and unlabelled data set;Establish inversion and forward network and carry out preheating training;Freeze network parameter and predict initial model, combined with physical forward operator and physical constraint term iterative optimization generates physical pseudo label;Unfreeze network parameter, introduce physical pseudo label into semi-supervised joint update training of inversion and forward network;Cyclically alternately execute physical pseudo label generation and semi-supervised joint update training until meeting stopping criterion, obtain trained inversion network;Finally, the poststack seismic record of research area is input into the network, and the wave impedance inversion result is output.The application overcomes the problem of insufficient physical constraint strength in semi-supervised inversion, and can still achieve high-precision wave impedance inversion under the condition of limited well labels.

Embodiments provide methods and systems for processing unstructured and unlabelled data. A method includes generating, by a processor, a structured and unlabelled training dataset from an unstructured and unlabelled dataset. The method includes categorizing the structured and unlabelled training dataset into a plurality of clusters by executing an unsupervised algorithm. Each cluster of a selected set of clusters from the plurality of clusters is labelled with an applicable label from a set of labels. The method includes executing a supervised algorithm to generate a trained supervised model using a labelled training dataset including the set of labels and an input dataset generated from plurality of datapoints present in each cluster of the selected set of clusters. The method includes generating a Labelled Data1 (LD1) by executing the trained supervised model configured to assign applicable label from the set of labels to each datapoint of the structured and unlabelled training dataset.