DISENTANGLED CYCLIC RECONSTRUCTION FOR DO-MAIN ADAPTATION

Abstract

The domain adaptation problem involves learning a unique classification or regression model capable of performing on both a source and a target domain. Although the labels for the source data are available during training, the labels in the target domain are unknown. An effective way to tackle this problem lies in extracting insightful features invariant to the source and target domains. In this work, we propose splitting the information for each domain into a task-related representation and its complimentary context representation. We propose an original method to disentangle these two representations in the single-domain supervised case. We then adapt this method to the unsupervised domain adaptation problem. In particular, our method allows disentanglement in the target domain, despite the absence of training labels. This enables the isolation of task-specific information from both domains and a projection into a common representation. The task-specific representation allows efficient transfer of knowledge acquired from the source domain to the target domain. We validate the proposed method on several classical domain adaptation benchmarks and illustrate the benefits of disentanglement for domain adaptation.

1. INTRODUCTION

The wide adoption of Deep Neural Networks in practical supervised learning applications is hindered by their sensitivity to the training data distribution. This problem, known as domain shift, can drastically weaken, in real-life operating conditions, the performance of a model that seemed perfectly efficient in simulation. Learning a model with the goal of making it robust to a specific domain shift is called domain adaptation (DA). Often, the data available to achieve DA consist of a labeled training set from a source domain and an unlabeled sample set from a target domain. This yields the problem of unsupervised domain adaptation (UDA). In this work, we take an information disentanglement perspective on UDA. We argue that a key to efficient UDA lies in separating the necessary information to complete the network's task (classification or regression), from a task-orthogonal information which we call context or style. Disentanglement in the target domain seems however a difficult endeavor since the available data is unlabeled. Our contribution is two-fold. We propose a formal definition of the disentanglement problem for UDA which, to the best of our knowledge, is new. Then we design a new learning method, called DiCyR (Disentangled Cyclic Reconstruction), which relies on cyclic reconstruction of inputs in order to achieve efficient disentanglement, including in the target domain. We derive DiCyR both in the supervised learning and in the UDA cases. This paper is organized as follows. Section 2 presents the required background on supervised learning and UDA, and proposes a definition of disentanglement for UDA. Section 3 reviews recent work in the literature that allow for a critical look at our contribution and put it in perspective. Section 4 introduces DiCyR, first for the single-domain supervised learning case and then for the UDA problem. Finally, Section 5 empirically evaluates DiCyR against state-of-the-art methods and discusses its strengths, weaknesses and variants. Section 6 summarizes and concludes this paper.

