18438320. INFERENCE APPARATUS, IMAGING APPARATUS, METHOD OF CONTROLLING INFERENCE APPARATUS, AND STORAGE MEDIUM simplified abstract (CANON KABUSHIKI KAISHA)
INFERENCE APPARATUS, IMAGING APPARATUS, METHOD OF CONTROLLING INFERENCE APPARATUS, AND STORAGE MEDIUM
Organization Name
Inventor(s)
REIJI Hasegawa of Kanagawa (JP)
INFERENCE APPARATUS, IMAGING APPARATUS, METHOD OF CONTROLLING INFERENCE APPARATUS, AND STORAGE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18438320 titled 'INFERENCE APPARATUS, IMAGING APPARATUS, METHOD OF CONTROLLING INFERENCE APPARATUS, AND STORAGE MEDIUM
The abstract describes an inference apparatus that utilizes machine learned models to output likelihoods of different classes and perform computations in parallel to improve efficiency.
- The apparatus includes a first machine learned model with multiple learners and a set of second machine learned models corresponding to different classes.
- Computation of the second machine learned model corresponding to a selected class is done simultaneously with the computation of the learners in the first machine learned model.
- If the selected class coincides with the class selected based on likelihoods after the first model's computation is complete, the computation of the second model continues for that class.
- This process results in an inference result based on the likelihoods calculated by the machine learned models.
Potential Applications: - This technology can be applied in various fields such as image recognition, natural language processing, and predictive analytics. - It can be used in autonomous vehicles, medical diagnosis, and fraud detection systems.
Problems Solved: - Enhances the efficiency of inference processes by running computations in parallel. - Improves accuracy in predicting classes by utilizing likelihoods calculated by multiple machine learned models.
Benefits: - Faster inference results due to parallel computation. - Higher accuracy in class predictions leading to better decision-making. - Scalable and adaptable to different applications and industries.
Commercial Applications: Title: Enhanced Inference Apparatus for Improved Decision-Making This technology can be commercialized in industries such as healthcare, finance, and e-commerce for tasks like medical image analysis, risk assessment, and personalized recommendations.
Prior Art: Researchers can explore prior studies on parallel computation in machine learning models and inference processes to understand the evolution of this technology.
Frequently Updated Research: Stay updated on advancements in parallel computation techniques in machine learning models and their applications in various industries.
Questions about Inference Apparatus: 1. How does the parallel computation of machine learned models improve the efficiency of the inference process? 2. What are the key factors to consider when selecting classes for simultaneous computation in the inference apparatus?
Original Abstract Submitted
An inference apparatus includes a first machine learned model including a plurality of learners and outputting likelihoods of a plurality of classes, and a plurality of second machine learned models corresponding to the plurality of classes, performs computation of a second machine learned model corresponding to a first class selected from the plurality of classes based on likelihoods calculated in the middle of computation of the plurality of learners in the first machine learned model, in parallel with remaining computation of the plurality of learners in the first machine learned model, and in a case where a second class selected based on likelihoods when computation of the plurality of learners in the first machine learned model is fully completed is coincident with the first class, continues the computation of the second machine learned model corresponding to the first class, thereby outputting an inference result.
