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Expanding the domain that deep neural network has already learned withoutaccessing old domain data is a challenging task because deep neural networksforget previously learned information when learning new data from a new domain.In this paper, we propose a less-forgetful learning method for the domainexpansion scenario. While existing domain adaptation techniques solely focusedon adapting to new domains, the proposed technique focuses on working well withboth old and new domains without needing to know whether the input is from theold or new domain. First, we present two naive approaches which will beproblematic, then we provide a new method using two proposed properties forless-forgetful learning. Finally, we prove the effectiveness of our methodthrough experiments on image classification tasks. All datasets used in thepaper, will be released on our website for someone's follow-up study.

Less-forgetful Learning for Domain Expansion in Deep Neural Networks