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Continual learning for image classification Anuvabh DuttTo cite this version: Anuvabh Dutt. Continual learning for image classification. Artificial Intelligence [cs.AI]. Universit Grenoble Alpes, 2019. English. NNT : 2019GREAM063. tel02907322HAL Id: tel02907322 https://theses.hal.science/tel02907322v1 Submitted on 27 Jul 2020HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not.

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Entities involved in developing or deploying image recognition systems that utilize continual learning techniques may be required to file continual learning for image. This includes researchers, developers, and companies that create machine learning models that evolve over time.

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To fill out continual learning for image, you typically need to provide details about the model architecture, datasets used, learning strategies employed, and performance metrics over time. It may also involve documenting how the model incorporates new data and mitigates forgetting of prior knowledge.

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The purpose of continual learning for image is to enable models to improve and adapt to new data without needing to be retrained from scratch. This capability allows for more efficient use of computational resources and helps maintain high accuracy as new information becomes available.

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Information that must be reported on continual learning for image includes the type of learning algorithms used, the new data being integrated, performance evaluations over time, strategies for avoiding catastrophic forgetting, and any changes in the model's architecture.

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