17945073. SYSTEMS, METHODS, AND APPARATUS FOR IMAGE CLASSIFICATION WITH DOMAIN INVARIANT REGULARIZATION simplified abstract (Samsung Electronics Co., Ltd.)

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SYSTEMS, METHODS, AND APPARATUS FOR IMAGE CLASSIFICATION WITH DOMAIN INVARIANT REGULARIZATION

Organization Name

Samsung Electronics Co., Ltd.

Inventor(s)

Behnam Babagholami Mohamadabadi of Escondido CA (US)

Mostafa El-khamy of San Diego CA (US)

Kee-Bong Song of San Diego CA (US)

SYSTEMS, METHODS, AND APPARATUS FOR IMAGE CLASSIFICATION WITH DOMAIN INVARIANT REGULARIZATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 17945073 titled 'SYSTEMS, METHODS, AND APPARATUS FOR IMAGE CLASSIFICATION WITH DOMAIN INVARIANT REGULARIZATION

Simplified Explanation

The patent application describes a system and method for processing images using a domain invariant machine learning model. Here is a simplified explanation of the abstract:

  • The system receives an input image.
  • It utilizes a domain invariant machine learning model to analyze the input image.
  • The machine learning model has been trained using domain invariant regularization, which helps it perform consistently across different domains or datasets.
  • Based on the analysis, the system computes an output.
  • The system then displays information based on the computed output.

Potential Applications

This technology can have various applications, including:

  • Image recognition: The system can be used to identify objects, scenes, or patterns in images.
  • Medical imaging: It can assist in analyzing medical images for diagnosis or research purposes.
  • Surveillance: The system can be employed in security systems to detect and classify objects or activities in surveillance footage.
  • Autonomous vehicles: It can contribute to the perception capabilities of self-driving cars, helping them understand and react to their surroundings.

Problems Solved

The technology addresses several challenges in image processing and machine learning, such as:

  • Domain shift: By using domain invariant regularization, the machine learning model can generalize well across different datasets, reducing the impact of variations in image characteristics.
  • Robustness: The system aims to provide consistent and reliable results, even when faced with diverse or challenging image inputs.
  • Efficiency: The method allows for efficient processing of images, enabling real-time or near-real-time applications.

Benefits

This technology offers several benefits:

  • Improved accuracy: The use of domain invariant regularization helps the machine learning model perform well across different domains, leading to more accurate results.
  • Versatility: The system can handle various types of images and adapt to different domains, making it applicable in a wide range of scenarios.
  • Time and cost savings: By automating image analysis and providing quick outputs, the system can save time and reduce the need for manual intervention.
  • Scalability: The method can be applied to large-scale image processing tasks, accommodating high volumes of data efficiently.


Original Abstract Submitted

A system and a method are disclosed for receiving an input image, using a domain invariant machine learning model to compute an output based on the input image, wherein the domain invariant machine learning model is trained using domain invariant regularization, and displaying information based on the output.

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