Patent ID: 11899710
Assignee: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 6:
7. A non-transitory computer-readable storage medium storing computer instructions; wherein the computer instructions are configured for causing a computer to perform operations of:
performing joint training on a first sub-network configured for recognition processing and a second sub-network configured for retrieval processing in a classification network by adopting an identical set of training data to obtain a trained target classification network, wherein, the first sub-network and the second sub-network are twin networks that are consistent in network structures and share a set of weights; and
inputting image data to be recognized into the target classification network to obtain a recognition result;
wherein the performing joint training on the first sub-network configured for recognition processing and the second sub-network configured for retrieval processing in the classification network by adopting the identical set of training data comprises:
for the first sub-network, training the first sub-network based on first sample data in the training data and classification labels for the corresponding data; and
for the second sub-network, randomly generating a first sample data pair based on the first sample data in the training data, and training the second sub-network based on the first sample data pair and an identification label of the corresponding data pair;
wherein, the computer instructions are further configured for causing computer to perform an operation of:
inputting a first feature output by a last one convolution layer of the first sub-network into a classifier to perform feature classification and then output a first loss function during training the first sub-network;
performing feature comparison between a second feature output by a last one convolution layer of the second sub-network and the first feature to output a second loss function during training the second sub-network obtaining a third loss function based on the first loss function and the second loss function; and
completing the joint training on the first sub-network and the second sub-network based on a back propagation of the third loss function to obtain the target classification network.