US 12,456,056 B2
Training method and device for generative adversarial network model, equipment, program and storage medium
Hao Liu, Beijing (CN); Jindong Han, Beijing (CN); Hengshu Zhu, Beijing (CN); and Dejing Dou, Beijing (CN)
Assigned to BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD., Beijing (CN)
Filed by BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD., Beijing (CN)
Filed on Dec. 6, 2021, as Appl. No. 17/457,903.
Claims priority of application No. 202011547686.8 (CN), filed on Dec. 24, 2020.
Prior Publication US 2022/0092433 A1, Mar. 24, 2022
Int. Cl. G06N 3/088 (2023.01); G01N 33/00 (2006.01); G01W 1/02 (2006.01); G06N 3/045 (2023.01)
CPC G06N 3/088 (2013.01) [G06N 3/045 (2023.01); G01N 33/0009 (2013.01); G01W 1/02 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A training method for a heterogeneous generative adversarial network model, executed by electronic equipment, wherein the heterogeneous generative adversarial network model comprises a generator and a discriminator, and the method comprises:
acquiring measurement data of a heterogeneous station, wherein the heterogeneous station comprises at least two types of stations, and each type of station among the at least two types of stations is configured to measure and obtain environment data corresponding to the each type as measurement data of the each type of station; and
setting the measurement data of the heterogeneous station as a training sample, and performing joint training on the heterogeneous generative adversarial network model according to a total objective function;
wherein the heterogeneous station comprises an air quality monitoring station and a weather monitoring station, and the measurement data comprises weather measurement data and air quality measurement data;
wherein the generator is configured to predict environment data at a future occasion according to environment data of the heterogeneous station at a historical occasion so as to output predicted data;
the discriminator is configured to be input the predicted data output by the generator and corresponding measurement data, and discriminate a similarity between the measurement data and the predicted data; and
the total objective function comprises a first objective function of the generator and a second objective function of the discriminator;
wherein the discriminator comprises at least one of:
a space discriminator, which is configured to be input predicted data output by the generator and corresponding measurement data of each station at a set occasion, and discriminate a space similarity between the measurement data and the predicted data at the set occasion;
a time discriminator, which is configured to be input predicted data output by the generator and corresponding measurement data of a set station at at least two occasions, and discriminate a time similarity between the measurement data and the predicted data of the set station; or
a macro discriminator, which is configured to be input predicted data output by the generator and corresponding measurement data of a plurality of stations at multiple occasions, and discriminate a macro similarity between the predicted data and the measurement data of the generator;
wherein the discriminator is provided with the second objective function.