In visual domain adaptation (DA), separating the domain-specific characteristics from the domain-invariant representations is an ill-posed problem. Existing methods apply different kinds of priors or directly minimize the domain discrepancy to address this problem, which lack flexibility in handling real-world situations. Another research pipeline expresses the domain-specific information as a gradual transferring process, which tends to be suboptimal in accurately removing the domain-specific properties. In this paper, we address the modeling of domain-invariant and domain-specific information from the heuristic search perspective. We identify the characteristics in the existing representations that lead to larger domain discrepancy as the heuristic representations.

Summary and Contributions: The paper presents a method inspired by heuristic search algorithms (such as A*) for the problem of Domain Adaptation (DA). The authors claim that to achieve domain invariant representations, one must explicitly model domain-specific characteristics. The ideal representation is considered as the goal and the intermediate domain-specific representations are regarded as the distance from the current representation to the ideal one. When the heuristic representations are near zero the terminal state is reached. To achieve this goal the authors propose a domain adaptation network made of a fundament network F and a heuristic network H (that is possibly divided into several subnetworks).

In visual domain adaptation (DA), separating the domain-specific characteristics from the domain-invariant representations is an ill-posed problem. Existing methods apply different kinds of priors or directly minimize the domain discrepancy to address this problem, which lack flexibility in handling real-world situations. Another research pipeline expresses the domain-specific information as a gradual transferring process, which tends to be suboptimal in accurately removing the domain-specific properties. In this paper, we address the modeling of domain-invariant and domain-specific information from the heuristic search perspective. We identify the characteristics in the existing representations that lead to larger domain discrepancy as the heuristic representations.