26,020 research outputs found
DDFlow: Learning Optical Flow with Unlabeled Data Distillation
We present DDFlow, a data distillation approach to learning optical flow
estimation from unlabeled data. The approach distills reliable predictions from
a teacher network, and uses these predictions as annotations to guide a student
network to learn optical flow. Unlike existing work relying on hand-crafted
energy terms to handle occlusion, our approach is data-driven, and learns
optical flow for occluded pixels. This enables us to train our model with a
much simpler loss function, and achieve a much higher accuracy. We conduct a
rigorous evaluation on the challenging Flying Chairs, MPI Sintel, KITTI 2012
and 2015 benchmarks, and show that our approach significantly outperforms all
existing unsupervised learning methods, while running at real time.Comment: 8 pages, AAAI 1
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
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