79 research outputs found
Low-Rank Subspace Override for Unsupervised Domain Adaptation
Current supervised learning models cannot generalize well across domain
boundaries, which is a known problem in many applications, such as robotics or
visual classification. Domain adaptation methods are used to improve these
generalization properties. However, these techniques suffer either from being
restricted to a particular task, such as visual adaptation, require a lot of
computational time and data, which is not always guaranteed, have complex
parameterization, or expensive optimization procedures. In this work, we
present an approach that requires only a well-chosen snapshot of data to find a
single domain invariant subspace. The subspace is calculated in closed form and
overrides domain structures, which makes it fast and stable in
parameterization. By employing low-rank techniques, we emphasize on descriptive
characteristics of data. The presented idea is evaluated on various domain
adaptation tasks such as text and image classification against state of the art
domain adaptation approaches and achieves remarkable performance across all
tasks
A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning
and related fields. This review asks the question: how can a classifier learn
from a source domain and generalize to a target domain? We present a
categorization of approaches, divided into, what we refer to as, sample-based,
feature-based and inference-based methods. Sample-based methods focus on
weighting individual observations during training based on their importance to
the target domain. Feature-based methods revolve around on mapping, projecting
and representing features such that a source classifier performs well on the
target domain and inference-based methods incorporate adaptation into the
parameter estimation procedure, for instance through constraints on the
optimization procedure. Additionally, we review a number of conditions that
allow for formulating bounds on the cross-domain generalization error. Our
categorization highlights recurring ideas and raises questions important to
further research.Comment: 20 pages, 5 figure
Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes
During the last half decade, convolutional neural networks (CNNs) have
triumphed over semantic segmentation, which is one of the core tasks in many
applications such as autonomous driving. However, to train CNNs requires a
considerable amount of data, which is difficult to collect and laborious to
annotate. Recent advances in computer graphics make it possible to train CNNs
on photo-realistic synthetic imagery with computer-generated annotations.
Despite this, the domain mismatch between the real images and the synthetic
data cripples the models' performance. Hence, we propose a curriculum-style
learning approach to minimize the domain gap in urban scenery semantic
segmentation. The curriculum domain adaptation solves easy tasks first to infer
necessary properties about the target domain; in particular, the first task is
to learn global label distributions over images and local distributions over
landmark superpixels. These are easy to estimate because images of urban scenes
have strong idiosyncrasies (e.g., the size and spatial relations of buildings,
streets, cars, etc.). We then train a segmentation network while regularizing
its predictions in the target domain to follow those inferred properties. In
experiments, our method outperforms the baselines on two datasets and two
backbone networks. We also report extensive ablation studies about our
approach.Comment: This is the extended version of the ICCV 2017 paper "Curriculum
Domain Adaptation for Semantic Segmentation of Urban Scenes" with additional
GTA experimen
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