The invention discloses a Wasserstein distance-based similar
adversarial network characterization model. Marginal probability distribution of source domain subjects and target domain subjects is reduced to the greatest extent by a method of reducing the Wasserstein distance,
conditional probability distribution is reduced by a correlation enhancement method, namely, the internal relation of categories is enhanced, the scheme includes the following steps: performing sampling, filtering
noise, performing mapping, setting a Wasserstein distance of
a domain obfuscator, setting a gradient penalty of the domain obfuscator, adopting an association enhanced classifier, solving the similarity of feature representation from a source domain to a target domain, solving the similarity of feature representation from the target domain to the source domain, obtaining the round-trip probability of features in the source domain and the target domain, calculating the
label probability of the source domain, calculating the loss of Lzw and Psts through
cross entropy loss, setting the access probability, setting the target domain
label probability, calculating the loss of Lop and Pv through
cross entropy loss, setting the classifier loss, setting the source
domain prediction classification loss, setting the number N of iterations, and when the number of times of training reaches the set number of iterations, stopping operation.