1,718 research outputs found
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
Traditional architectures for solving computer vision problems and the degree
of success they enjoyed have been heavily reliant on hand-crafted features.
However, of late, deep learning techniques have offered a compelling
alternative -- that of automatically learning problem-specific features. With
this new paradigm, every problem in computer vision is now being re-examined
from a deep learning perspective. Therefore, it has become important to
understand what kind of deep networks are suitable for a given problem.
Although general surveys of this fast-moving paradigm (i.e. deep-networks)
exist, a survey specific to computer vision is missing. We specifically
consider one form of deep networks widely used in computer vision -
convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN
and then examine the broad variations proposed over time to suit different
applications. We hope that our recipe-style survey will serve as a guide,
particularly for novice practitioners intending to use deep-learning techniques
for computer vision.Comment: Published in Frontiers in Robotics and AI (http://goo.gl/6691Bm
SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-world Verification
An open research problem in automatic signature verification is the skilled
forgery attacks. However, the skilled forgeries are very difficult to acquire
for representation learning. To tackle this issue, this paper proposes to learn
dynamic signature representations through ranking synthesized signatures.
First, a neuromotor inspired signature synthesis method is proposed to
synthesize signatures with different distortion levels for any template
signature. Then, given the templates, we construct a lightweight
one-dimensional convolutional network to learn to rank the synthesized samples,
and directly optimize the average precision of the ranking to exploit relative
and fine-grained signature similarities. Finally, after training, fixed-length
representations can be extracted from dynamic signatures of variable lengths
for verification. One highlight of our method is that it requires neither
skilled nor random forgeries for training, yet it surpasses the
state-of-the-art by a large margin on two public benchmarks.Comment: To appear in AAAI 202
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