  Real-world perception systems in many cases build on hardware with limited
resources to adhere to cost and power limitations of their carrying system.
Deploying deep neural networks on resource-constrained hardware became possible
with model compression techniques, as well as efficient and hardware-aware
architecture design. However, model adaptation is additionally required due to
the diverse operation environments. In this work, we address the problem of
training deep neural networks on resource-constrained hardware in the context
of visual domain adaptation. We select the task of monocular depth estimation
where our goal is to transform a pre-trained model to the target's domain data.
While the source domain includes labels, we assume an unlabelled target domain,
as it happens in real-world applications. Then, we present an adversarial
learning approach that is adapted for training on the device with limited
resources. Since visual domain adaptation, i.e. neural network training, has
not been previously explored for resource-constrained hardware, we present the
first feasibility study for image-based depth estimation. Our experiments show
that visual domain adaptation is relevant only for efficient network
architectures and training sets at the order of a few hundred samples. Models
and code are publicly available.
