2 research outputs found
Domain Agnostic Internal Distributions for Unsupervised Model Adaptation
We develop an algorithm for sequential adaptation of a classifier that is
trained for a source domain to generalize in a unannotated target domain. We
consider that the model has been trained on the source domain annotated data
and then it needs to be adapted using the target domain unannotated data when
the source domain data is not accessible. We align the distributions of the
source and the target domains in a discriminative embedding space via an
intermediate internal distribution. This distribution is estimated using the
source data representations in the embedding space. We provide theoretical
analysis and conduct extensive experiments on several benchmarks to demonstrate
the proposed method is effective