公开(公告)号：US20240296668A1

公开(公告)日：2024-09-05

申请号：US18572510

申请日：2021-09-10

Applicant: Intel Corporation

Inventor： Dongqi Cai ,  Yurong Chen ,  Anbang Yao

IPC: G06V10/82 ,  G06V10/94

CPC classification number: G06V10/82 ,  G06V10/955

Abstract: Technology to conduct image sequence/video analysis can include a processor, and a memory coupled to the processor, the memory storing a neural network, the neural network comprising a plurality of convolution layers, and a plurality of normalization layers arranged as a relay structure, wherein each normalization layer is coupled to and following a respective one of the plurality of convolution layers. The plurality of normalization layers can be arranged as a relay structure where a normalization layer for a layer (k) is coupled to and following a normalization layer for a preceding layer (k−1). The normalization layer for the layer (k) is coupled to the normalization layer for the preceding layer (k−1) via a hidden state signal and a cell state signal, each signal generated by the normalization layer for the preceding layer (k−1). Each normalization layer (k) can include a meta-gating unit (MGU) structure.

公开(公告)号：US11934949B2

公开(公告)日：2024-03-19

申请号：US16973608

申请日：2018-09-27

Applicant: Intel Corporation

Inventor： Jianguo Li ,  Yurong Chen ,  Zheng Wang

IPC: G06N3/08 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/084

CPC classification number: G06N3/08 ,  G06N3/044 ,  G06N3/045 ,  G06N3/063 ,  G06N3/084

Abstract: Embodiments are directed to a composite binary decomposition network. An embodiment of a computer-readable storage medium includes executable computer program instructions for transforming a pre-trained first neural network into a binary neural network by processing layers of the first neural network in a composite binary decomposition process, where the first neural network having floating point values representing weights of various layers of the first neural network. The composite binary decomposition process includes a composite operation to expand real matrices or tensors into a plurality of binary matrices or tensors, and a decompose operation to decompose one or more binary matrices or tensors of the plurality of binary matrices or tensors into multiple lower rank binary matrices or tensors.

公开(公告)号：US11907843B2

公开(公告)日：2024-02-20

申请号：US16305626

申请日：2016-06-30

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Yiwen Guo ,  Yurong Chen

IPC: G06N3/082 ,  G06F18/241 ,  G06V10/764 ,  G06V10/82

CPC classification number: G06N3/082 ,  G06F18/241 ,  G06V10/764 ,  G06V10/82

Abstract: Systems, apparatuses and methods may provide for conducting an importance measurement of a plurality of parameters in a trained neural network and setting a subset of the plurality of parameters to zero based on the importance measurement. Additionally, the pruned neural network may be re-trained. In one example, conducting the importance measurement includes comparing two or more parameter values that contain covariance matrix information.

公开(公告)号：US11887001B2

公开(公告)日：2024-01-30

申请号：US16328182

申请日：2016-09-26

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Yiwen Guo ,  Lin Xu ,  Yan Lin ,  Yurong Chen

IPC: G06N3/08 ,  G06N3/082 ,  G06N3/084 ,  G06N3/02 ,  G06N3/045 ,  G06F17/16 ,  G06N3/04 ,  G06N3/044

CPC classification number: G06N3/082 ,  G06F17/16 ,  G06N3/02 ,  G06N3/04 ,  G06N3/045 ,  G06N3/084 ,  G06N3/044

Abstract: An apparatus and method are described for reducing the parameter density of a deep neural network (DNN). A layer-wise pruning module to prune a specified set of parameters from each layer of a reference dense neural network model to generate a second neural network model having a relatively higher sparsity rate than the reference neural network model; a retraining module to retrain the second neural network model in accordance with a set of training data to generate a retrained second neural network model; and the retraining module to output the retrained second neural network model as a final neural network model if a target sparsity rate has been reached or to provide the retrained second neural network model to the layer-wise pruning model for additional pruning if the target sparsity rate has not been reached.

公开(公告)号：US11823033B2

公开(公告)日：2023-11-21

申请号：US17058078

申请日：2018-09-13

Applicant: Intel Corporation

Inventor： Yurong Chen ,  Jianguo Li

IPC: G06K9/00 ,  G06N3/063 ,  G06N3/04 ,  G06N3/08 ,  G06V40/16 ,  G06F18/213 ,  G06V10/82 ,  G06V10/32

CPC classification number: G06N3/063 ,  G06F18/213 ,  G06N3/04 ,  G06N3/08 ,  G06V10/32 ,  G06V10/82 ,  G06V40/161 ,  G06V40/168 ,  G06V40/172

Abstract: Techniques related to implementing convolutional neural networks for face or other object recognition are discussed. Such techniques may include applying, in turn, a depth-wise separable convolution, a condense point-wise convolution, and an expansion point-wise convolution to input feature maps to generate output feature maps such that the output from the expansion point-wise convolution has more channels than the output from the condense point-wise convolution.

公开(公告)号：US11790631B2

公开(公告)日：2023-10-17

申请号：US17408094

申请日：2021-08-20

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Yun Ren ,  Hao Zhao ,  Tao Kong ,  Yurong Chen

IPC: G06V10/00 ,  G06V10/44 ,  G06N3/04 ,  G06N3/08 ,  G06V30/24 ,  G06F18/243 ,  G06V30/19 ,  G06V10/82 ,  G06V20/70 ,  G06V20/10

CPC classification number: G06V10/454 ,  G06F18/24317 ,  G06N3/04 ,  G06N3/08 ,  G06V10/82 ,  G06V20/10 ,  G06V20/70 ,  G06V30/19173 ,  G06V30/2504

Abstract: An example apparatus for mining multi-scale hard examples includes a convolutional neural network to receive a mini-batch of sample candidates and generate basic feature maps. The apparatus also includes a feature extractor and combiner to generate concatenated feature maps based on the basic feature maps and extract the concatenated feature maps for each of a plurality of received candidate boxes. The apparatus further includes a sample scorer and miner to score the candidate samples with multi-task loss scores and select candidate samples with multi-task loss scores exceeding a threshold score.

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

IPC: G06N3/04 ,  G06N3/08

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.

公开(公告)号：US11157764B2

公开(公告)日：2021-10-26

申请号：US16489084

申请日：2017-03-27

Applicant: INTEL CORPORATION

Inventor： Libin Wang ,  Anbang Yao ,  Jianguo Li ,  Yurong Chen

IPC: G06K9/62 ,  G06K9/46 ,  G06N3/04

Abstract: An example apparatus for semantic image segmentation includes a receiver to receive an image to be segmented. The apparatus also includes a gated dense pyramid network comprising a plurality of gated dense pyramid (GDP) blocks to be trained to generate semantic labels for each pixel in the received image. The apparatus further includes a generator to generate a segmented image based on the generated semantic labels.

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

IPC: G06K9/62 ,  G06K9/00 ,  G06K9/66 ,  G06N3/08

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.

公开(公告)号：US10929977B2

公开(公告)日：2021-02-23

申请号：US16320944

申请日：2016-08-25

Applicant: Intel Corporation

Inventor： Libin Wang ,  Anbang Yao ,  Yurong Chen

IPC: G06K9/00 ,  G06T7/10 ,  G06K9/46 ,  G06N3/04 ,  G06N3/08 ,  G06K9/34 ,  G06T7/11 ,  G06T7/143 ,  G06F16/55 ,  G06N5/04

Abstract: Techniques related to implementing fully convolutional networks for semantic image segmentation are discussed. Such techniques may include combining feature maps from multiple stages of a multi-stage fully convolutional network to generate a hyper-feature corresponding to an input image, up-sampling the hyper-feature and summing it with a feature map of a previous stage to provide a final set of features, and classifying the final set of features to provide semantic image segmentation of the input image.