210,593 research outputs found
Ground Metric Learning on Graphs
Optimal transport (OT) distances between probability distributions are
parameterized by the ground metric they use between observations. Their
relevance for real-life applications strongly hinges on whether that ground
metric parameter is suitably chosen. Selecting it adaptively and
algorithmically from prior knowledge, the so-called ground metric learning GML)
problem, has therefore appeared in various settings. We consider it in this
paper when the learned metric is constrained to be a geodesic distance on a
graph that supports the measures of interest. This imposes a rich structure for
candidate metrics, but also enables far more efficient learning procedures when
compared to a direct optimization over the space of all metric matrices. We use
this setting to tackle an inverse problem stemming from the observation of a
density evolving with time: we seek a graph ground metric such that the OT
interpolation between the starting and ending densities that result from that
ground metric agrees with the observed evolution. This OT dynamic framework is
relevant to model natural phenomena exhibiting displacements of mass, such as
for instance the evolution of the color palette induced by the modification of
lighting and materials.Comment: Fixed sign of gradien
Dynamic Metric Learning from Pairwise Comparisons
Recent work in distance metric learning has focused on learning
transformations of data that best align with specified pairwise similarity and
dissimilarity constraints, often supplied by a human observer. The learned
transformations lead to improved retrieval, classification, and clustering
algorithms due to the better adapted distance or similarity measures. Here, we
address the problem of learning these transformations when the underlying
constraint generation process is nonstationary. This nonstationarity can be due
to changes in either the ground-truth clustering used to generate constraints
or changes in the feature subspaces in which the class structure is apparent.
We propose Online Convex Ensemble StrongLy Adaptive Dynamic Learning (OCELAD),
a general adaptive, online approach for learning and tracking optimal metrics
as they change over time that is highly robust to a variety of nonstationary
behaviors in the changing metric. We apply the OCELAD framework to an ensemble
of online learners. Specifically, we create a retro-initialized composite
objective mirror descent (COMID) ensemble (RICE) consisting of a set of
parallel COMID learners with different learning rates, demonstrate RICE-OCELAD
on both real and synthetic data sets and show significant performance
improvements relative to previously proposed batch and online distance metric
learning algorithms.Comment: to appear Allerton 2016. arXiv admin note: substantial text overlap
with arXiv:1603.0367
Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification
This paper addresses the task of zero-shot image classification. The key
contribution of the proposed approach is to control the semantic embedding of
images -- one of the main ingredients of zero-shot learning -- by formulating
it as a metric learning problem. The optimized empirical criterion associates
two types of sub-task constraints: metric discriminating capacity and accurate
attribute prediction. This results in a novel expression of zero-shot learning
not requiring the notion of class in the training phase: only pairs of
image/attributes, augmented with a consistency indicator, are given as ground
truth. At test time, the learned model can predict the consistency of a test
image with a given set of attributes , allowing flexible ways to produce
recognition inferences. Despite its simplicity, the proposed approach gives
state-of-the-art results on four challenging datasets used for zero-shot
recognition evaluation.Comment: in ECCV 2016, Oct 2016, amsterdam, Netherlands. 201
Learned Perceptual Image Enhancement
Learning a typical image enhancement pipeline involves minimization of a loss
function between enhanced and reference images. While L1 and L2 losses are
perhaps the most widely used functions for this purpose, they do not
necessarily lead to perceptually compelling results. In this paper, we show
that adding a learned no-reference image quality metric to the loss can
significantly improve enhancement operators. This metric is implemented using a
CNN (convolutional neural network) trained on a large-scale dataset labelled
with aesthetic preferences of human raters. This loss allows us to conveniently
perform back-propagation in our learning framework to simultaneously optimize
for similarity to a given ground truth reference and perceptual quality. This
perceptual loss is only used to train parameters of image processing operators,
and does not impose any extra complexity at inference time. Our experiments
demonstrate that this loss can be effective for tuning a variety of operators
such as local tone mapping and dehazing
Self-supervised AutoFlow
Recently, AutoFlow has shown promising results on learning a training set for
optical flow, but requires ground truth labels in the target domain to compute
its search metric. Observing a strong correlation between the ground truth
search metric and self-supervised losses, we introduce self-supervised AutoFlow
to handle real-world videos without ground truth labels. Using self-supervised
loss as the search metric, our self-supervised AutoFlow performs on par with
AutoFlow on Sintel and KITTI where ground truth is available, and performs
better on the real-world DAVIS dataset. We further explore using
self-supervised AutoFlow in the (semi-)supervised setting and obtain
competitive results against the state of the art
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