5,183 research outputs found
A Survey on Metric Learning for Feature Vectors and Structured Data
The need for appropriate ways to measure the distance or similarity between
data is ubiquitous in machine learning, pattern recognition and data mining,
but handcrafting such good metrics for specific problems is generally
difficult. This has led to the emergence of metric learning, which aims at
automatically learning a metric from data and has attracted a lot of interest
in machine learning and related fields for the past ten years. This survey
paper proposes a systematic review of the metric learning literature,
highlighting the pros and cons of each approach. We pay particular attention to
Mahalanobis distance metric learning, a well-studied and successful framework,
but additionally present a wide range of methods that have recently emerged as
powerful alternatives, including nonlinear metric learning, similarity learning
and local metric learning. Recent trends and extensions, such as
semi-supervised metric learning, metric learning for histogram data and the
derivation of generalization guarantees, are also covered. Finally, this survey
addresses metric learning for structured data, in particular edit distance
learning, and attempts to give an overview of the remaining challenges in
metric learning for the years to come.Comment: Technical report, 59 pages. Changes in v2: fixed typos and improved
presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new
method
Self-Supervised Feature Learning by Learning to Spot Artifacts
We introduce a novel self-supervised learning method based on adversarial
training. Our objective is to train a discriminator network to distinguish real
images from images with synthetic artifacts, and then to extract features from
its intermediate layers that can be transferred to other data domains and
tasks. To generate images with artifacts, we pre-train a high-capacity
autoencoder and then we use a damage and repair strategy: First, we freeze the
autoencoder and damage the output of the encoder by randomly dropping its
entries. Second, we augment the decoder with a repair network, and train it in
an adversarial manner against the discriminator. The repair network helps
generate more realistic images by inpainting the dropped feature entries. To
make the discriminator focus on the artifacts, we also make it predict what
entries in the feature were dropped. We demonstrate experimentally that
features learned by creating and spotting artifacts achieve state of the art
performance in several benchmarks.Comment: CVPR 2018 (spotlight
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