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公开(公告)号：US20230093823A1
公开(公告)日：2023-03-30
申请号：US17041340
申请日：2019-12-18
Applicant: Intel Corporation
Inventor： Anbang Yao , Ping Hu , Yangyuxuan Kang , Yurong Chen
Abstract: Methods, apparatus, systems, and articles of manufacture for modifying a machine learning model are disclosed. An example apparatus includes a supervised branch inserter to insert a supervised branch into a machine learning model at an identified location, a first cluster generator to generate a first cluster of the inserted supervised branch using a first clustering technique, a second cluster generator to generate a second cluster of the inserted supervised branch using a second clustering technique, the second clustering technique different from the first clustering technique, a cluster joiner to join the first cluster and the second cluster to form a clustering block, the clustering block appended to an end of the supervised branch, and a propagation strategy executor to execute a propagation training strategy to modify a parameter of the machine learning model.
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公开(公告)号：US11157727B2
公开(公告)日：2021-10-26
申请号：US16647722
申请日：2017-12-27
Applicant: INTEL CORPORATION
Inventor： Ping Hu , Anbang Yao , Jia Wei , Dongqi Cai , Yurong Chen
Abstract: Techniques are provided for neural network based, human attribute recognition, guided by anatomical key-points and statistic correlation models. Attributes include characteristics that can be visibly identified or inferred from an image, such as gender, hairstyle, clothing style, etc. A methodology implementing the techniques according to an embodiment includes applying an attribute feature extraction (AFE) convolutional neural network (CNN) to an image of a human to generate attribute feature maps based on the image. The method further includes applying a key-point guided proposal (KPG) CNN to the image of the human to generate proposed hierarchical regions of the image based on associated anatomical key-points. The method further includes generating recognition probabilities for the human attributes using a CNN combination layer that incorporates the attribute feature maps, the proposed hierarchical regions, and statistical correlation models (SCMs) which provide correlations between the features of the attribute feature maps and the proposed hierarchical regions.
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公开(公告)号：US20200226362A1
公开(公告)日：2020-07-16
申请号：US16647722
申请日：2017-12-27
Applicant: INTEL CORPORATION
Inventor： Ping Hu , Anbang Yao , Jia Wei , Dongqi Cai , Yurong Chen
Abstract: Techniques are provided for neural network based, human attribute recognition, guided by anatomical key-points and statistic correlation models. Attributes include characteristics that can be visibly identified or inferred from an image, such as gender, hairstyle, clothing style, etc. A methodology implementing the techniques according to an embodiment includes applying an attribute feature extraction (AFE) convolutional neural network (CNN) to an image of a human to generate attribute feature maps based on the image. The method further includes applying a key-point guided proposal (KPG) CNN to the image of the human to generate proposed hierarchical regions of the image based on associated anatomical key-points. The method further includes generating recognition probabilities for the human attributes using a CNN combination layer that incorporates the attribute feature maps, the proposed hierarchical regions, and statistical correlation models (SCMs) which provide correlations between the features of the attribute feature maps and the proposed hierarchical regions.
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公开(公告)号：US11537851B2
公开(公告)日：2022-12-27
申请号：US16475075
申请日：2017-04-07
Applicant: INTEL CORPORATION
Inventor： Yiwen Guo , Anbang Yao , Dongqi Cai , Libin Wang , Lin Xu , Ping Hu , Shandong Wang , Wenhua Cheng , Yurong Chen
Abstract: Methods and systems are disclosed using improved training and learning for deep neural networks. In one example, a deep neural network includes a plurality of layers, and each layer has a plurality of nodes. The nodes of each L layer in the plurality of layers are randomly connected to nodes of an L+1 layer. The nodes of each L+1 layer are connected to nodes in a subsequent L layer in a one-to-one manner. Parameters related to the nodes of each L layer are fixed. Parameters related to the nodes of each L+1 layers are updated. In another example, inputs for the input layer and labels for the output layer of a deep neural network are determined related to a first sample. A similarity between different pairs of inputs and labels is estimated using a Gaussian regression process.
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公开(公告)号：US20220222492A1
公开(公告)日：2022-07-14
申请号：US17584216
申请日：2022-01-25
Applicant: Intel Corporation
Inventor： Yiwen GUO , Yuqing Hou , Anbang Yao , Dongqi Cai , Lin Xu , Ping Hu , Shandong Wang , Wenhua Cheng , Yurong Chen , Libin Wang
Abstract: Methods and systems for budgeted and simplified training of deep neural networks (DNNs) are disclosed. In one example, a trainer is to train a DNN using a plurality of training sub-images derived from a down-sampled training image. A tester is to test the trained DNN using a plurality of testing sub-images derived from a down-sampled testing image. In another example, in a recurrent deep Q-network (RDQN) having a local attention mechanism located between a convolutional neural network (CNN) and a long-short time memory (LSTM), a plurality of feature maps are generated by the CNN from an input image. Hard-attention is applied by the local attention mechanism to the generated plurality of feature maps by selecting a subset of the generated feature maps. Soft attention is applied by the local attention mechanism to the selected subset of generated feature maps by providing weights to the selected subset of generated feature maps in obtaining weighted feature maps. The weighted feature maps are stored in the LSTM. A Q value is calculated for different actions based on the weighted feature maps stored in the LSTM.
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Patent Agency Ranking
公开(公告)号：US11263490B2
公开(公告)日：2022-03-01
申请号：US16475078
申请日：2017-04-07
Applicant: INTEL CORPORATION
Inventor： Yiwen Guo , Yuqing Hou , Anbang Yao , Dongqi Cai , Lin Xu , Ping Hu , Shandong Wang , Wenhua Cheng , Yurong Chen , Libin Wang
Abstract: Methods and systems for budgeted and simplified training of deep neural networks (DNNs) are disclosed. In one example, a trainer is to train a DNN using a plurality of training sub-images derived from a down-sampled training image. A tester is to test the trained DNN using a plurality of testing sub-images derived from a down-sampled testing image. In another example, in a recurrent deep Q-network (RDQN) having a local attention mechanism located between a convolutional neural network (CNN) and a long-short time memory (LSTM), a plurality of feature maps are generated by the CNN from an input image. Hard-attention is applied by the local attention mechanism to the generated plurality of feature maps by selecting a subset of the generated feature maps. Soft attention is applied by the local attention mechanism to the selected subset of generated feature maps by providing weights to the selected subset of generated feature maps in obtaining weighted feature maps. The weighted feature maps are stored in the LSTM. A Q value is calculated for different actions based on the weighted feature maps stored in the LSTM.
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公开(公告)号：US11176632B2
公开(公告)日：2021-11-16
申请号：US16474540
申请日：2017-04-07
Applicant: INTEL CORPORATION
Inventor： Anbang Yao , Dongqi Cai , Libin Wang , Lin Xu , Ping Hu , Shandong Wang , Wenhua Cheng , Yiwen Guo , Liu Yang , Yuqing Hou , Zhou Su
Abstract: Described herein are advanced artificial intelligence agents for modeling physical interactions. An apparatus to provide an active artificial intelligence (AI) agent includes at least one database to store physical interaction data and compute cluster coupled to the at least one database. The compute cluster automatically obtains physical interaction data from a data collection module without manual interaction, stores the physical interaction data in the at least one database, and automatically trains diverse sets of machine learning program units to simulate physical interactions with each individual program unit having a different model based on the applied physical interaction data.
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公开(公告)号：US11151361B2
公开(公告)日：2021-10-19
申请号：US16471106
申请日：2017-01-20
Applicant: INTEL CORPORATION
Inventor： Anbang Yao , Dongqi Cai , Ping Hu , Shandong Wang , Yurong Chen
Abstract: An apparatus for dynamic emotion recognition in unconstrained scenarios is described herein. The apparatus comprises a controller to pre-process image data and a phase-convolution mechanism to build lower levels of a CNN such that the filters form pairs in phase. The apparatus also comprises a phase-residual mechanism configured to build middle layers of the CNN via plurality of residual functions and an inception-residual mechanism to build top layers of the CNN by introducing multi-scale feature extraction. Further, the apparatus comprises a fully connected mechanism to classify extracted features.
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公开(公告)号：US20210201078A1
公开(公告)日：2021-07-01
申请号：US16475079
申请日：2017-04-07
Applicant: INTEL CORPORATION
Inventor： Anbang Yao , Shandong Wang , Wenhua Cheng , Dongqi Cai , Libin Wang , Lin Xu , Ping Hu , Yiwen Guo , Liu Yang , Yuging Hou , Zhou Su , Yurong Chen
Abstract: Methods and systems for advanced and augmented training of deep neural networks (DNNs) using synthetic data and innovative generative networks. A method includes training a DNN using synthetic data, training a plurality of DNNs using context data, associating features of the DNNs trained using context data with features of the DNN trained with synthetic data, and generating an augmented DNN using the associated features.
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公开(公告)号：US20200026999A1
公开(公告)日：2020-01-23
申请号：US16475076
申请日：2017-04-07
Applicant: INTEL CORPORATION
Inventor： Libin Wang , Yiwen Guo , Anbang Yao , Dongqi Cai , Lin Xu , Ping Hu , Shangong Wang , Wenhua Cheng , Yurong Chen
Abstract: Methods and systems are disclosed for boosting deep neural networks for deep learning. In one example, in a deep neural network including a first shallow network and a second shallow network, a first training sample is processed by the first shallow network using equal weights. A loss for the first shallow network is determined based on the processed training sample using equal weights. Weights for the second shallow network are adjusted based on the determined loss for the first shallow network. A second training sample is processed by the second shallow network using the adjusted weights. In another example, in a deep neural network including a first weak network and a second weak network, a first subset of training samples is processed by the first weak network using initialized weights. A classification error for the first weak network on the first subset of training samples is determined. The second weak network is boosted using the determined classification error of the first weak network with adjusted weights. A second subset of training samples is processed by the second weak network using the adjusted weights.
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