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We present a new semi-supervised domain adaptation framework that combines anovel auto-encoder-based domain adaptation model with a simultaneous learningscheme providing stable improvements over state-of-the-art domain adaptationmodels. Our framework holds strong distribution matching property by trainingboth source and target auto-encoders using a novel simultaneous learning schemeon a single graph with an optimally modified MMD loss objective function.Additionally, we design a semi-supervised classification approach bytransferring the aligned domain invariant feature spaces from source domain tothe target domain. We evaluate on three datasets and show proof that ourframework can effectively solve both fragile convergence (adversarial) and weakdistribution matching problems between source and target feature space(discrepancy) with a high `speed' of adaptation requiring a very low number ofiterations.

Semi-Supervised Domain Adaptation with Auto-Encoder via Simultaneous Learning