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An agent-based model (ABM) simulates actions and interactions of the synthetic agents to understand the system-level behaviour. The synthetic population, the key input to ABM, mimics the distribution of the individual-level attributes in the actual population. Since individual-level attributes of the entire population are unavailable, small-scale samples are generally used for population synthesis. Synthesizing the population by directly sampling from the small-scale samples ignores the possible attribute combinations that are observed in the actual population but do not exist in the small-scale samples, called ‘sampling zeros’. A deep generative model (DGM) can potentially synthesize the sampling zeros but at the expense of falsely generating the infeasible attribute combinations that should be ‘zero’ in the generated data but exist, called ‘structural zeros’. This study proposes a novel method to ensure that the …