Semi-Supervised Domain Adaptation with Auto-Encoder via Simultaneous Learning
This work addresses domain adaptation for machine learning applications where labeled data is scarce, offering incremental improvements in stability and efficiency.
The paper tackles the problem of semi-supervised domain adaptation by proposing a framework that combines an auto-encoder-based model with a simultaneous learning scheme, resulting in stable improvements over state-of-the-art models and effective resolution of convergence and distribution matching issues with high adaptation speed and low iterations.
We present a new semi-supervised domain adaptation framework that combines a novel auto-encoder-based domain adaptation model with a simultaneous learning scheme providing stable improvements over state-of-the-art domain adaptation models. Our framework holds strong distribution matching property by training both source and target auto-encoders using a novel simultaneous learning scheme on a single graph with an optimally modified MMD loss objective function. Additionally, we design a semi-supervised classification approach by transferring the aligned domain invariant feature spaces from source domain to the target domain. We evaluate on three datasets and show proof that our framework can effectively solve both fragile convergence (adversarial) and weak distribution matching problems between source and target feature space (discrepancy) with a high `speed' of adaptation requiring a very low number of iterations.