18165083. CLASSIFICATION MODEL TRAINING METHOD, HYPERPARAMETER SEARCH METHOD, AND APPARATUS simplified abstract (Huawei Technologies Co., Ltd.)

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CLASSIFICATION MODEL TRAINING METHOD, HYPERPARAMETER SEARCH METHOD, AND APPARATUS

Organization Name

Huawei Technologies Co., Ltd.

Inventor(s)

Yucong Zhou of Shenzhen (CN)

Zhao Zhong of Shenzhen (CN)

CLASSIFICATION MODEL TRAINING METHOD, HYPERPARAMETER SEARCH METHOD, AND APPARATUS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18165083 titled 'CLASSIFICATION MODEL TRAINING METHOD, HYPERPARAMETER SEARCH METHOD, AND APPARATUS

Simplified Explanation

The patent application describes a method and apparatus for training a classification model using artificial intelligence technologies. The method involves obtaining a target hyperparameter that controls the gradient update operation of the classification model. The classification model includes a scaling invariance linear layer, which ensures that the predicted classification result remains unchanged when the weight parameter is multiplied by any scaling coefficient.

  • The method involves obtaining a target hyperparameter for controlling the gradient update operation of a classification model.
  • The classification model includes a scaling invariance linear layer that maintains the predicted classification result unchanged regardless of the scaling coefficient applied to the weight parameter.
  • The weight parameter of the classification model is updated based on the target hyperparameter and a target training manner to obtain a trained classification model.

Potential applications of this technology:

  • This technology can be applied in various fields that require classification models, such as image recognition, natural language processing, and fraud detection.
  • It can be used in autonomous vehicles for object recognition and classification.
  • This technology can be utilized in healthcare for disease diagnosis and prediction.

Problems solved by this technology:

  • The scaling invariance linear layer ensures that the predicted classification result remains consistent regardless of the scaling coefficient applied to the weight parameter, improving the reliability of the classification model.
  • The hyperparameter search method helps in finding the optimal hyperparameter for training the classification model, leading to improved performance and accuracy.

Benefits of this technology:

  • The method and apparatus provide a more robust and reliable classification model by incorporating the scaling invariance linear layer.
  • The hyperparameter search method helps in optimizing the training process, resulting in improved performance and accuracy.
  • This technology can be applied to various domains, providing a versatile solution for classification tasks.


Original Abstract Submitted

This application relates to the field of artificial intelligence technologies, and describes a classification model training method, a hyperparameter search method, and an apparatus. The training method includes obtaining a target hyperparameter of a to-be-trained classification model. The target hyperparameter is used to control a gradient update operation of the to-be-trained classification model. The to-be-trained classification model includes a scaling invariance linear layer. The scaling invariance linear layer enables a predicted classification result output when a weight parameter of the to-be-trained classification model is multiplied by any scaling coefficient to remain unchanged. The method further includes updating the weight parameter of the to-be-trained classification model based on the target hyperparameter and a target training manner, to obtain a trained classification model.

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