Multi-task based lifelong learning
AssigneeSamsung Electronics Co., Ltd.
InventorsJie Zhang, Junting Zhang, Shalini Ghosh, Dawei Li, Jingwen Zhu
Life of this patent
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Abstract
Methods, devices, and computer-readable media for multi-task based lifelong learning. A method for lifelong learning includes identifying a new task for a machine learning model to perform. The machine learning model trained to perform an existing task. The method includes adaptively training a network architecture of the machine learning model to generate an adapted machine learning model based on incorporating inherent correlations between the new task and the existing task. The method further includes using the adapted machine learning model to perform both the existing task and the new task.
Claims20
A method for lifelong learning, the method comprising:
- identifying a new task for a machine learning model to perform, the machine learning model trained to perform an existing task;
- adaptively training a network architecture of the machine learning model to generate an adapted machine learning model based on incorporating inherent correlations between the new task and the existing task, wherein adaptively training the network architecture includes:
- generating a plurality of child network architectures, wherein each of the plurality of child network architectures is expanded from a size of the network architecture by at least one of:
- adding one or more new layers to the network architecture or expanding one or more existing layers of the network architecture;
- and
- determining an optimal child network architecture from the plurality of child network architectures for the adapted machine learning model;
- and
- using the adapted machine learning model to perform both the existing task and the new task.
An electronic device for lifelong learning, the electronic device comprising:
- a memory configured to store a machine learning model trained to perform an existing task;
- and
- a processor operably connected to the memory, the processor configured to:
- identify a new task for the machine learning model to perform;
- adaptively train a network architecture of the machine learning model to generate an adapted machine learning model based on incorporating inherent correlations between the new task and the existing task, wherein, to adaptively train the network architecture, the processor is configured to:
- generate a plurality of child network architectures, wherein each of the plurality of child network architectures is expanded from a size of the network architecture by at least one of:
- adding one or more new layers to the network architecture or expanding one or more existing layers of the network architecture;
- and
- determine an optimal child network architecture from the plurality of child network architectures for the adapted machine learning model;
- and
- use the adapted machine learning model to perform both the existing task and the new task.
A non-transitory, computer-readable medium comprising program code for lifelong learning that, when executed by a processor of an electronic device, causes the electronic device to:
- identify a new task for a machine learning model to perform, the machine learning model trained to perform an existing task;
- adaptively train a network architecture of the machine learning model to generate an adapted machine learning model based on incorporating inherent correlations between the new task and the existing task;
- and
- use the adapted machine learning model to perform both the existing task and the new task;
- wherein the program code that, when executed by the processor, causes the electronic device to adaptively train the network architecture comprises program code that, when executed by the processor, causes the electronic device to:
- generate a plurality of child network architectures, wherein each of the plurality of child network architectures is expanded from a size of the network architecture by at least one of:
- adding one or more new layers to the network architecture or expanding one or more existing layers of the network architecture;
- and
- determine an optimal child network architecture from the plurality of child network architectures for the adapted machine learning model.
Dependencies are inferred from the claim text.