公开(公告)号：US11423260B1

公开(公告)日：2022-08-23

申请号：US17589888

申请日：2022-01-31

申请人： Harbin Institute of Technology (Shenzhen) (Shenzhen Institute of Science and Technology Innovation, Harbin Institute of Technology) ,  Dongguan University of Technology

发明人： Qing Liao ,  Binxing Fang ,  Ye Ding

IPC分类号： G06K9/62

摘要： The present disclosure provides an anomaly detection method and apparatus for multi-type data. According to the anomaly detection method for multi-type data, an adversarial learning network is trained, so that a generator in the adversarial learning network fits a distribution of a normal training sample and learns a potential mode of the normal training sample, to obtain an updated adversarial learning network, an anomaly evaluation function in the updated adversarial learning network is constructed according to a reconstruction error generated during training, and the updated adversarial learning network is constructed into an anomaly detection model, to perform anomaly detection on inputted detection data by the anomaly detection model, to obtain an anomaly detection result. A mode classifier is introduced to effectively resolve difficult anomaly detection when a distribution of detected data is similar to that of normal data, further improving the accuracy of anomaly detection.

公开(公告)号：US20220261600A1

公开(公告)日：2022-08-18

申请号：US17589888

申请日：2022-01-31

申请人： Harbin Institute of Technology (Shenzhen) (Shenzhen Institute of Science and Technology Innovation ,  Dongguan University of Technology

发明人： Qing Liao ,  Binxing Fang ,  Ye Ding

IPC分类号： G06K9/62

摘要： The present disclosure provides an anomaly detection method and apparatus for multi-type data. According to the anomaly detection method for multi-type data, an adversarial learning network is trained, so that a generator in the adversarial learning network fits a distribution of a normal training sample and learns a potential mode of the normal training sample, to obtain an updated adversarial learning network, an anomaly evaluation function in the updated adversarial learning network is constructed according to a reconstruction error generated during training, and the updated adversarial learning network is constructed into an anomaly detection model, to perform anomaly detection on inputted detection data by the anomaly detection model, to obtain an anomaly detection result. A mode classifier is introduced to effectively resolve difficult anomaly detection when a distribution of detected data is similar to that of normal data, further improving the accuracy of anomaly detection.

公开(公告)号：US11436451B2

公开(公告)日：2022-09-06

申请号：US17577099

申请日：2022-01-17

申请人： Harbin Institute of Technology (Shenzhen) (Shenzhen Institute of Science and Technology Innovation, Harbin Institute of Technology) ,  Dongguan University of Technology

发明人： Qing Liao ,  Ye Ding ,  Binxing Fang ,  Xuan Wang

IPC分类号： G06K9/62

摘要： The present disclosure provides a multimodal fine-grained mixing method and system, a device, and a storage medium. The method includes: extracting data features from multimodal graphic and textual data, and obtaining each composition of the data features, the data features including a visual regional feature and a text word feature; performing fine-grained classification on modal information of each composition of the data features, to obtain classification results; and performing inter-modal and intra-modal information fusion on each composition according to the classification results, to obtain a fusion feature. The method enables a multimodal model to utilize a complementary characteristic of the multimodal data, with no influence by irrelevant information.

公开(公告)号：US11170786B1

公开(公告)日：2021-11-09

申请号：US17334790

申请日：2021-05-30

申请人： Harbin Institute of Technology (Shenzhen) (Shenzhen Institute of Science and Technology Innovation, Harbin Institute of Technology)

发明人： Qing Liao ,  Yangqian Wang ,  Yang Liu ,  Lin Jiang ,  Xuan Wang ,  Ye Wang

IPC分类号： G10L17/06 ,  G06N20/00 ,  G10L17/04 ,  G10L17/02

摘要： The present disclosure proposes a federated speaker verification method based on differential privacy, including: 1. performing, by a server, UBM pre-training to obtain an initial UBM; 2. receiving, by the client, the pre-trained initial UBM, and performing initial UBM learning based on local private speech data; 3. performing, by the client, differential privacy protection based on learned statistics; 4. aggregating, by the server, statistics uploaded by multiple clients, and updating the initial UBM; and 5. receiving, by the client, the updated UBM, performing adjustment based on the local private speech data to obtain a GMM for a user of the client, and determining, based on the updated UBM and the GMM, whether a to-be-verified speech is generated by the user of the client.

公开(公告)号：US20220237420A1

公开(公告)日：2022-07-28

申请号：US17577099

申请日：2022-01-17

申请人： Harbin Inst. of Tech (Shenzhen) (Shenzhen Inst. of Science and Tech Innovation, Harbin Inst. of Tech ,  Dongguan University of Technology

发明人： Qing Liao ,  Ye Ding ,  Binxing Fang ,  Xuan Wang

IPC分类号： G06K9/62

摘要： The present disclosure provides a multimodal fine-grained mixing method and system, a device, and a storage medium. The method includes: extracting data features from multimodal graphic and textual data, and obtaining each composition of the data features, the data features including a visual regional feature and a text word feature; performing fine-grained classification on modal information of each composition of the data features, to obtain classification results; and performing inter-modal and intra-modal information fusion on each composition according to the classification results, to obtain a fusion feature. The method enables a multimodal model to utilize a complementary characteristic of the multimodal data, with no influence by irrelevant information.