In the last decade, deep neural networks became state-of-the-art in many computer vision tasks. Nevertheless, their performances are affected when test data are acquired in environments visually different from the data used at training time. Recent domain adaptation techniques are efficient to mitigate this problem but they assume that target data distributions are fixed and available in a batch setting. These limitations severely constrain potential applications. In ODACE, we consider the scenario of an autonomous device, such as a car or a robot, navigating in a continuously changing environment. In this scenario, the different vision tasks are performed using a deep neural network. We focus more specifically on structured prediction tasks such as depth estimation and instance segmentation. In this project, we propose to develop new types of deep learning algorithms where the neural network parameters are continuously adapted to handle the current visual environment. The goal is to design dynamic mechanisms that can online adapt the network representations without human supervision using only the video frames from the current environment. In this scenario, we identify four main challenges. First, because of the sequential nature of data collection in the current environment, the model disposes of a few samples only with limited visual variability. This partial knowledge about the current environment affects the performance of standard domain adaptation methods. This problem is referred to as partial domain adaptation. Second, in ODACE, we will specifically address structured prediction problems. In this case, we claim that adaptation should be constrained in such a way that it leads to predictions with coherent structures. Third, in the case of deep neural networks, parameters are updated via Stochastic Gradient Descent (SGD). In our online setting, SGD is problematic since it is computationally costly and it assumes that training samples are independent and identically distributed while video frames are usually correlated. Consequently, new online optimization methods must be designed to obtain a fast online adaptation. Forth, we assume that the device may encounter environments similar to previously seen environments. Therefore, we propose models capable of benefiting from past adaptation experiences in order to adapt faster to the current environment. This ability to continually learn over time by retaining previously learned experiences is referred to as continual learning. Unfortunately, neural networks tend to forget previously learned information. Solutions have been proposed to alleviate this catastrophic forgetting problem but they are generally limited to classification settings. Therefore, we aim at designing new continual learning methods for structured prediction tasks that can address the online setting. To illustrate the large range of potential applications of our approaches, the proposed methods will be evaluated on four different use-cases corresponding to two tasks (depth estimation and instance segmentation) performed in two different scenarios (autonomous driving and robot navigation). To validate the methods developed in this project, we plan to record a new dataset for depth estimation in the case of a continuously changing environment.