View source for 17966568. SYSTEMS AND METHODS FOR ARTIFICIAL-INTELLIGENCE MODEL TRAINING USING UNSUPERVISED DOMAIN ADAPTATION WITH MULTI-SOURCE META-DISTILLATION simplified abstract (Huawei Technologies Co., Ltd.)

=SYSTEMS AND METHODS FOR ARTIFICIAL-INTELLIGENCE MODEL TRAINING USING UNSUPERVISED DOMAIN ADAPTATION WITH MULTI-SOURCE META-DISTILLATION=


==Organization Name==

[[:Category:Huawei Technologies Co., Ltd.|Huawei Technologies Co., Ltd.]]


[[Category:Huawei Technologies Co., Ltd.]]

==Inventor(s)==

[[:Category:Zhixiang Chi of Markham (CA)|Zhixiang Chi of Markham (CA)]][[Category:Zhixiang Chi of Markham (CA)]]

[[:Category:Li Gu of Markham (CA)|Li Gu of Markham (CA)]][[Category:Li Gu of Markham (CA)]]

[[:Category:Tao Zhong of Markham (CA)|Tao Zhong of Markham (CA)]][[Category:Tao Zhong of Markham (CA)]]

[[:Category:Yuanhao Yu of Markham (CA)|Yuanhao Yu of Markham (CA)]][[Category:Yuanhao Yu of Markham (CA)]]

[[:Category:Yang Wang of Markham (CA)|Yang Wang of Markham (CA)]][[Category:Yang Wang of Markham (CA)]]

[[:Category:Jin Tang of Markham (CA)|Jin Tang of Markham (CA)]][[Category:Jin Tang of Markham (CA)]]

==SYSTEMS AND METHODS FOR ARTIFICIAL-INTELLIGENCE MODEL TRAINING USING UNSUPERVISED DOMAIN ADAPTATION WITH MULTI-SOURCE META-DISTILLATION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17966568 titled 'SYSTEMS AND METHODS FOR ARTIFICIAL-INTELLIGENCE MODEL TRAINING USING UNSUPERVISED DOMAIN ADAPTATION WITH MULTI-SOURCE META-DISTILLATION

==Simplified Explanation==

The abstract of the patent application describes a method that involves the following steps:

* Obtaining a set of training samples from one or more domains.
* Using the set of training samples to query multiple artificial-intelligence (AI) models.
* Combining the outputs of the queried AI models.
* Adapting a target AI model using knowledge distillation with the combined outputs.

Potential applications of this technology:

* Improving the performance of AI models by leveraging knowledge from multiple domains.
* Enhancing the accuracy and reliability of AI systems in various fields such as image recognition, natural language processing, and recommendation systems.

Problems solved by this technology:

* Overcoming the limitations of training AI models on a single domain by incorporating knowledge from multiple domains.
* Addressing the challenge of generalizing AI models to perform well on diverse datasets and real-world scenarios.

Benefits of this technology:

* Increased accuracy and robustness of AI models due to the combination of outputs from multiple models.
* Improved transfer learning capabilities, allowing AI models to apply knowledge learned from one domain to another.
* Enhanced efficiency in training AI models by leveraging pre-trained models and distilling their knowledge into a target model.


==Original Abstract Submitted==

A method has the steps of obtaining a set of training samples from one or more domains, using the set of training samples to query a plurality of artificial-intelligence (AI) models, combining the outputs of the queried AI models, and adapting a target AI model via knowledge distillation using the combined outputs.



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