7,641 research outputs found
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
Self-Supervised Relative Depth Learning for Urban Scene Understanding
As an agent moves through the world, the apparent motion of scene elements is
(usually) inversely proportional to their depth. It is natural for a learning
agent to associate image patterns with the magnitude of their displacement over
time: as the agent moves, faraway mountains don't move much; nearby trees move
a lot. This natural relationship between the appearance of objects and their
motion is a rich source of information about the world. In this work, we start
by training a deep network, using fully automatic supervision, to predict
relative scene depth from single images. The relative depth training images are
automatically derived from simple videos of cars moving through a scene, using
recent motion segmentation techniques, and no human-provided labels. This proxy
task of predicting relative depth from a single image induces features in the
network that result in large improvements in a set of downstream tasks
including semantic segmentation, joint road segmentation and car detection, and
monocular (absolute) depth estimation, over a network trained from scratch. The
improvement on the semantic segmentation task is greater than those produced by
any other automatically supervised methods. Moreover, for monocular depth
estimation, our unsupervised pre-training method even outperforms supervised
pre-training with ImageNet. In addition, we demonstrate benefits from learning
to predict (unsupervised) relative depth in the specific videos associated with
various downstream tasks. We adapt to the specific scenes in those tasks in an
unsupervised manner to improve performance. In summary, for semantic
segmentation, we present state-of-the-art results among methods that do not use
supervised pre-training, and we even exceed the performance of supervised
ImageNet pre-trained models for monocular depth estimation, achieving results
that are comparable with state-of-the-art methods
Real-to-Virtual Domain Unification for End-to-End Autonomous Driving
In the spectrum of vision-based autonomous driving, vanilla end-to-end models
are not interpretable and suboptimal in performance, while mediated perception
models require additional intermediate representations such as segmentation
masks or detection bounding boxes, whose annotation can be prohibitively
expensive as we move to a larger scale. More critically, all prior works fail
to deal with the notorious domain shift if we were to merge data collected from
different sources, which greatly hinders the model generalization ability. In
this work, we address the above limitations by taking advantage of virtual data
collected from driving simulators, and present DU-drive, an unsupervised
real-to-virtual domain unification framework for end-to-end autonomous driving.
It first transforms real driving data to its less complex counterpart in the
virtual domain and then predicts vehicle control commands from the generated
virtual image. Our framework has three unique advantages: 1) it maps driving
data collected from a variety of source distributions into a unified domain,
effectively eliminating domain shift; 2) the learned virtual representation is
simpler than the input real image and closer in form to the "minimum sufficient
statistic" for the prediction task, which relieves the burden of the
compression phase while optimizing the information bottleneck tradeoff and
leads to superior prediction performance; 3) it takes advantage of annotated
virtual data which is unlimited and free to obtain. Extensive experiments on
two public driving datasets and two driving simulators demonstrate the
performance superiority and interpretive capability of DU-drive
Causally Regularized Learning with Agnostic Data Selection Bias
Most of previous machine learning algorithms are proposed based on the i.i.d.
hypothesis. However, this ideal assumption is often violated in real
applications, where selection bias may arise between training and testing
process. Moreover, in many scenarios, the testing data is not even available
during the training process, which makes the traditional methods like transfer
learning infeasible due to their need on prior of test distribution. Therefore,
how to address the agnostic selection bias for robust model learning is of
paramount importance for both academic research and real applications. In this
paper, under the assumption that causal relationships among variables are
robust across domains, we incorporate causal technique into predictive modeling
and propose a novel Causally Regularized Logistic Regression (CRLR) algorithm
by jointly optimize global confounder balancing and weighted logistic
regression. Global confounder balancing helps to identify causal features,
whose causal effect on outcome are stable across domains, then performing
logistic regression on those causal features constructs a robust predictive
model against the agnostic bias. To validate the effectiveness of our CRLR
algorithm, we conduct comprehensive experiments on both synthetic and real
world datasets. Experimental results clearly demonstrate that our CRLR
algorithm outperforms the state-of-the-art methods, and the interpretability of
our method can be fully depicted by the feature visualization.Comment: Oral paper of 2018 ACM Multimedia Conference (MM'18
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