On-the-fly deep learning in machine learning at autonomous machines
AssigneeIntel Corporation
InventorRAANAN YONATAN YEHEZKEL ROHEKAR
Life of this patent
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Abstract
A mechanism is described for facilitating the transfer of features learned by a context independent pre-trained deep neural network to a context dependent neural network. The mechanism includes extracting a feature learned by a first deep neural network (DNN) model via the framework, wherein the first DNN model is a pre-trained DNN model for computer vision to enable context-independent classification of an object within an input video frame and training, via the deep learning framework, a second DNN model for computer vision based on the extracted feature, the second DNN model an update of the first DNN model, wherein training the second DNN model includes training the second DNN model based on a dataset including context-dependent data.
Claims20
A data processing system on a computing device, the data processing system comprising:
- one or more storage devices comprising a graphics execution environment including instructions to provide a deep learning framework to accelerate deep learning operations via one or more general-purpose graphics processors of the computing device, the deep learning framework to cause the one or more general-purpose graphics processors to perform operations comprising:
- extracting, via the deep learning framework, a feature learned by a first deep neural network (DNN) model via the framework, wherein the first DNN model is a pre-trained DNN model for computer vision to enable context-independent classification of an object within an input video frame;
- and
- training, via the deep learning framework, a second DNN model for computer vision based on the extracted feature and a dataset including context-dependent data, the second DNN model an update of the first DNN model, wherein the deep learning framework is to provide a library of machine learning primitives, the machine learning primitives accelerated via instructions executed by the one or more general-purpose graphics processors, and training the second DNN model includes training the second DNN model via one or more primitives provided by the deep learning framework.
A method comprising:
- extracting, by one or more general-purpose graphics processors of a data processing system, a feature learned by a first deep neural network (DNN) model, wherein the first DNN model is a pre-trained DNN model for computer vision to enable context-independent classification of an object within an input video frame, instructions executed by the general-purpose processor to extract the feature are provided via a deep learning framework, the deep learning framework is provided by a graphics execution environment on a server computing device, and the deep learning framework provides instructions to accelerate deep learning operations;
- and
- training, via the deep learning framework, a second DNN model for computer vision based on the extracted feature and a dataset including context-dependent data, the second DNN model an update of the first DNN model, wherein the deep learning framework is to provide a library of machine learning primitives, the machine learning primitives accelerated via instructions executed by the one or more general-purpose graphics processors and training the second DNN model includes training the second DNN model via one or more primitives provided by the deep learning framework.
A non-transitory machine-readable medium storing instructions which, when executed by one or more processors including one or more general-purpose graphics processors, cause the one or more processors to perform operations comprising:
- extracting, by the one or more general-purpose graphics processors, a feature learned by a first deep neural network (DNN) model, wherein the first DNN model is a pre-trained DNN model for computer vision to enable context-independent classification of an object within an input video frame, the instructions executed by the general-purpose processor to extract the feature are provided via a deep learning framework, the deep learning framework is provided by a graphics execution environment on a server computing device, and the deep learning framework provides instructions to accelerate deep learning operations;
- and
- training, via the deep learning framework, a second DNN model for computer vision based on the extracted feature and a dataset including context-dependent data, the second DNN model an update of the first DNN model, wherein the deep learning framework is to provide a library of machine learning primitives, the machine learning primitives accelerated via instructions executed by the one or more general-purpose graphics processors and training the second DNN model includes training the second DNN model via one or more primitives provided by the deep learning framework.
Dependencies are inferred from the claim text.