Unified Multi-Domain Learning and Data Imputation using Adversarial Autoencoder

15 Mar 2020Andre MendesJulian TogeliusLeandro dos Santos Coelho

We present a novel framework that can combine multi-domain learning (MDL), data imputation (DI) and multi-task learning (MTL) to improve performance for classification and regression tasks in different domains. The core of our method is an adversarial autoencoder that can: (1) learn to produce domain-invariant embeddings to reduce the difference between domains; (2) learn the data distribution for each domain and correctly perform data imputation on missing data... (read more)

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