  Deep neural networks exhibit exceptional accuracy when they are trained and
tested on the same data distributions. However, neural classifiers are often
extremely brittle when confronted with domain shift---changes in the input
distribution that occur over time. We present ACE, a framework for semantic
segmentation that dynamically adapts to changing environments over the time. By
aligning the distribution of labeled training data from the original source
domain with the distribution of incoming data in a shifted domain, ACE
synthesizes labeled training data for environments as it sees them. This
stylized data is then used to update a segmentation model so that it performs
well in new environments. To avoid forgetting knowledge from past environments,
we introduce a memory that stores feature statistics from previously seen
domains. These statistics can be used to replay images in any of the previously
observed domains, thus preventing catastrophic forgetting. In addition to
standard batch training using stochastic gradient decent (SGD), we also
experiment with fast adaptation methods based on adaptive meta-learning.
Extensive experiments are conducted on two datasets from SYNTHIA, the results
demonstrate the effectiveness of the proposed approach when adapting to a
number of tasks.
