20240086684.METHOD AND DEVICE WITH TRAINING DATABASE CONSTRUCTION simplified abstract (samsung electronics co., ltd.)
Contents
- 1 METHOD AND DEVICE WITH TRAINING DATABASE CONSTRUCTION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND DEVICE WITH TRAINING DATABASE CONSTRUCTION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
METHOD AND DEVICE WITH TRAINING DATABASE CONSTRUCTION
Organization Name
Inventor(s)
Kyunghyun Cho of New York NY (US)
METHOD AND DEVICE WITH TRAINING DATABASE CONSTRUCTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240086684 titled 'METHOD AND DEVICE WITH TRAINING DATABASE CONSTRUCTION
Simplified Explanation
The abstract describes an electronic device that utilizes a machine learning-based conditional generative model to reconstruct target data from latent vectors, extrapolate new data based on existing data sets, and generate a new dataset with augmented target data.
- The device includes processors and memory storing instructions for implementing a machine learning-based conditional generative model.
- The model is trained on an existing data set for a target task.
- It determines an extrapolation weight to generate augmented latent vectors and condition data.
- It generates a new dataset with augmented target data based on the extrapolated data.
Potential Applications
This technology could be applied in various fields such as image and video generation, data augmentation for training machine learning models, and data synthesis for research purposes.
Problems Solved
This technology addresses the need for generating new data sets with augmented information for training machine learning models, improving model performance, and enhancing data diversity.
Benefits
The benefits of this technology include improved data synthesis capabilities, enhanced model training efficiency, and increased accuracy in generating new data sets for various applications.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of AI-powered tools for data augmentation in industries such as healthcare, finance, and marketing.
Possible Prior Art
One possible prior art for this technology could be existing machine learning models for data generation and augmentation, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
Unanswered Questions
How does this technology compare to existing data augmentation techniques in terms of efficiency and accuracy?
This article does not provide a direct comparison between this technology and other data augmentation techniques. Further research or experimentation may be needed to determine the effectiveness of this technology in comparison to existing methods.
What are the potential limitations or challenges in implementing this technology on a larger scale or in real-world applications?
The article does not address potential limitations or challenges that may arise when implementing this technology in practical settings. Additional studies or case studies may be necessary to identify and overcome any obstacles in deploying this technology commercially.
Original Abstract Submitted
an electronic device includes one or more processors and a memory storing instructions configured to, when executed by the one or more processors, cause the one or more processors to: implement a machine learning-based conditional generative model configured to reconstruct target data from latent vectors, the conditional generative model trained based on an existing data set for a target task; determine an extrapolation weight; generate an augmented latent vector and augmented condition data by extrapolating, based on the extrapolation weight, from a latent vector corresponding to the existing dataset and from existing condition data corresponding to the existing dataset; and generate a new dataset comprising augmented target data generated by the conditional generative model based on the augmented condition data and based on the augmented latent vector.
