Unsupervised learning with synthetic data and attention masks

A system, comprising:

• at least one processor;
• and
• at least one memory communicatively coupled to the at least one processor and comprising computer-readable instructions that upon execution by the at least one processor cause the at least one processor to perform acts comprising:
• generating a plurality of sample images by providing a plurality of text prompts into a trained generative model, respectively;
• for a sample image of the plurality of sample images, obtaining at least one attention map from a generative model, the at least one attention map being determined by the generative model for generating the sample image, an attention map indicating visual elements of an object within the sample image;
• and
• performing training of a target model according to unsupervised learning at least based on the plurality of sample images and attention maps for the plurality of sample images, the target model being configured to perform an image processing task.

A non-transitory computer-readable storage medium, storing computer-readable instructions that upon execution by a device cause the device to perform acts comprising:

• generating a plurality of sample images by providing a plurality of text prompts into a trained generative model, respectively;
• for a sample image of the plurality of sample images, obtaining at least one attention map from a generative model, the at least one attention map being determined by the generative model for generating the sample image, an attention map indicating visual elements of an object within the sample image;
• and
• performing training of a target model according to unsupervised learning at least based on the plurality of sample images and attention maps for the plurality of sample images, the target model being configured to perform an image processing task.