Unsupervised domain adaptation (UDA) for semantic segmentation has been attracting attention recently, as it could be beneficial for various label-scarce real-world scenarios (e.g., robot control, autonomous driving, medical imaging, etc.). Despite the significant progress in this field, current works mainly focus on a single-source single-target setting, which cannot handle more practical settings of multiple targets or even unseen targets. In this paper, we investigate open compound domain adaptation (OCDA), which deals with mixed and novel situations at the same time, for semantic segmentation. We present a novel framework based on three main design principles: discover, hallucinate, and adapt. The scheme first clusters compound target data based on style, discovering multiple latent domains (discover).

A.1, we evaluate our framework on two new datasets, Synscapes and SYNTHIA, A.2, we conduct additional ablation studies on the adaptation step using four latent A.3, we analyze hyperparameter K selection. A.4, we show more qualitative results. A.5, we elaborate the implementation details. The adaptation results are summarized in the Table 1. In the main paper, we already show that the proposed domain-wise adversaries are more effective than the traditional UDA approaches.

Unsupervised domain adaptation (UDA) for semantic segmentation has been attracting attention recently, as it could be beneficial for various label-scarce real-world scenarios (e.g., robot control, autonomous driving, medical imaging, etc.). Despite the significant progress in this field, current works mainly focus on a single-source single-target setting, which cannot handle more practical settings of multiple targets or even unseen targets. In this paper, we investigate open compound domain adaptation (OCDA), which deals with mixed and novel situations at the same time, for semantic segmentation. We present a novel framework based on three main design principles: discover, hallucinate, and adapt. The scheme first clusters compound target data based on style, discovering multiple latent domains (discover).

Imagine we want to train a self-driving car in New York so that we can take it all the way to Seattle without tediously driving it for over 48 hours. We hope our car can handle all kinds of environments on the trip and send us safely to the destination. We know that road conditions and views can be very different. It is intuitive to simply collect road data of this trip, let the car learn from every possible condition, and hope it becomes the perfect self-driving car for our New York to Seattle trip. It needs to understand the traffic and skyscrapers in big cities like New York and Chicago, more unpredictable weather in Seattle, mountains and forests in Montana, and all kinds of country views, farmlands, animals, etc. However, how much data is enough?

Imagine we want to train a self-driving car in New York so that we can take it all the way to Seattle without tediously driving it for over 48 hours. We hope our car can handle all kinds of environments on the trip and send us safely to the destination. We know that road conditions and views can be very different. It is intuitive to simply collect road data of this trip, let the car learn from every possible condition, and hope it becomes the perfect self-driving car for our New York to Seattle trip. It needs to understand the traffic and skyscrapers in big cities like New York and Chicago, more unpredictable weather in Seattle, mountains and forests in Montana, and all kinds of country views, farmlands, animals, etc.

Existing works on domain adaptation often assume clear boundaries between source and target domains. Despite giving rise to a clean problem formalization, such form falls short of simulating the real world where domains are compounded of interleaving and confounding factors, blurring the domain boundaries. In this work, we opt for a different problem, dubbed open compound domain adaptation (OCDA), for studying the techniques of training domain-robust models in a more realistic setting. OCDA considers a compound (unlabeled) target domain which mixes several major factors (e.g., backgrounds, lighting conditions, etc.), along with a labeled training set, in the training stage and new open domains during inference. The compound target domain can be seen as a combination of multiple traditional target domains each with its own idiosyncrasy. To tackle OCDA, we propose a class-confusion loss to disentangle the domain-dominant factors out of the data and then use them to schedule a curriculum domain adaptation strategy. Moreover, we use a memory-augmented neural network architecture to increase the network's capacity for handling previously unseen domains. Extensive experiments on digit classification, facial expression recognition, semantic segmentation, and reinforcement learning verify the effectiveness of our approach.