Method, device, and storage medium for targeted adversarial discriminative domain adaptation

A targeted adversarial discriminative domain adaptation (T-ADDA) device, comprising:

• a memory, configured to store program instructions for performing a T-ADDA method;
• and
• a processor, coupled with the memory and, when executing the program instructions, configured for:
• pre-training a source model, including a source feature encoder and a source classifier, on a source domain image dataset according to combined cross-entropy loss and center loss functions, wherein source feature vectors in each source class are generated;
• adapting a target feature encoder by:
• configuring the pre-trained source feature encoder of the pre-trained source model to be an initial target feature encoder, and using the initial target feature encoder to generate target feature vectors in each target class based on a target domain image dataset;
• adjusting the domain discriminator according to an adversarial domain discrimination loss using the source feature vectors in each source class and the target feature vectors in each target class;
• adjusting the initial target feature encoder according to a generative adversarial network (GAN) loss using the target feature vectors in each target class and the adjusted domain discriminator;
• and
• further adjusting the initial target feature encoder to generate the target feature encoder according to a feature class matching loss using labeled target feature vectors and corresponding source feature class centers;
• and
• generating a target model by concatenating the adapted target feature encoder with the pre-trained source classifier of the pre-trained source model.

A non-transitory computer-readable storage medium, containing program instructions for, when being executed by a processor, performing a targeted adversarial discriminative domain adaptation (T-ADDA) method, the method comprising:

• pre-training a source model, including a source feature encoder and a source classifier, on a source domain image dataset according to combined cross-entropy loss and center loss functions, wherein source feature vectors in each source class are generated;
• adapting a target feature encoder by:
• configuring the pre-trained source feature encoder of the pre-trained source model to be an initial target feature encoder, and using the initial target feature encoder to generate target feature vectors in each target class based on a target domain image dataset;
• adjusting the domain discriminator according to an adversarial domain discrimination loss using the source feature vectors in each source class and the target feature vectors in each target class;
• adjusting the initial target feature encoder according to a generative adversarial network (GAN) loss using the target feature vectors in each target class and the adjusted domain discriminator;
• and
• further adjusting the initial target feature encoder to generate the target feature encoder according to a feature class matching loss using labeled target feature vectors and corresponding source feature class centers;
• and
• generating a target model by concatenating the adapted target feature encoder with the pre-trained source classifier of the pre-trained source model.