17541588. GENERATING TASK-SPECIFIC TRAINING DATA simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

GENERATING TASK-SPECIFIC TRAINING DATA

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Lokesh Nagalapatti of Chennai (IN)

Ruhi Sharma Mittal of Bangalore (IN)

Sambaran Bandyopadhyay of Hooghly (IN)

Ramasuri Narayanam of Guntur (IN)

GENERATING TASK-SPECIFIC TRAINING DATA - A simplified explanation of the abstract

This abstract first appeared for US patent application 17541588 titled 'GENERATING TASK-SPECIFIC TRAINING DATA

Simplified Explanation

The patent application describes techniques for generating training data for machine learning models. Here is a simplified explanation of the abstract:

• The patent application discloses methods for creating synthetic data instances to train machine learning models.
• These synthetic data instances are generated based on one or more downstream tasks that the machine learning model needs to perform.
• The value of each synthetic data instance is determined with respect to the specific task it corresponds to.
• Based on these values, additional synthetic data instances are generated to further train the machine learning model.

Potential applications of this technology:

• This technology can be applied in various fields where machine learning models are used, such as computer vision, natural language processing, and robotics.
• It can be used to generate large amounts of diverse training data, which is crucial for training accurate and robust machine learning models.
• This technology can be particularly useful in scenarios where obtaining real-world training data is difficult, expensive, or time-consuming.

Problems solved by this technology:

• Generating high-quality training data is often a challenging and resource-intensive task in machine learning.
• This technology addresses the problem of limited or insufficient training data by creating synthetic data instances that can effectively train machine learning models.
• It helps overcome the problem of data scarcity, especially in domains where collecting real-world data is impractical or costly.

Benefits of this technology:

• By generating synthetic data instances, this technology enables the creation of larger and more diverse training datasets, leading to improved model performance.
• It reduces the reliance on real-world data, which can be limited or biased, by providing a way to generate synthetic data that covers a wide range of scenarios.
• The ability to generate synthetic data instances tailored to specific tasks allows for more targeted and efficient training of machine learning models.

Original Abstract Submitted

Techniques for generating machine learning training data which corresponds to one or more downstream tasks are disclosed. In one example, a computer implemented method comprises generating one or more synthetic data instances for training a machine learning model, and determining a value of respective ones of the one or more synthetic data instances with respect to at least one task. One or more additional synthetic data instances for training the machine learning model are generated based at least in part on the values of the respective ones of the one or more synthetic data instances.