1,589 research outputs found
Quantum Particle Swarm Optimization Algorithm Based on Dynamic Adaptive Search Strategy
The particle swarm system simulates the evolution of the social mechanism. In this system, the individual particle representing the potential solution flies in the multidimensional space in order to find the better or the optimal solution. But because of the search path and limited speed, it's hard to avoid local best and the premature phenomenon occurs easily. Based on the uncertain principle of the quantum mechanics, the global search ability of the quantum particle swarm optimization (QPSO) algorithms are better than the particle swarm optimization algorithm (PSO). On the basis of the fundamental quantum PSO algorithm, this article introduces the grouping optimization strategy, and meanwhile adopts the dynamic adjustment, quantum mutation and possibility acceptance criteria to improve the global search capability of the algorithm and avoid premature convergence phenomenon. By optimizing the test functions, the experimental simulation shows that the proposed algorithm has better global convergence and search ability
Online Deep Metric Learning
Metric learning learns a metric function from training data to calculate the
similarity or distance between samples. From the perspective of feature
learning, metric learning essentially learns a new feature space by feature
transformation (e.g., Mahalanobis distance metric). However, traditional metric
learning algorithms are shallow, which just learn one metric space (feature
transformation). Can we further learn a better metric space from the learnt
metric space? In other words, can we learn metric progressively and nonlinearly
like deep learning by just using the existing metric learning algorithms? To
this end, we present a hierarchical metric learning scheme and implement an
online deep metric learning framework, namely ODML. Specifically, we take one
online metric learning algorithm as a metric layer, followed by a nonlinear
layer (i.e., ReLU), and then stack these layers modelled after the deep
learning. The proposed ODML enjoys some nice properties, indeed can learn
metric progressively and performs superiorly on some datasets. Various
experiments with different settings have been conducted to verify these
properties of the proposed ODML.Comment: 9 page
A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification
Unsupervised cross-domain person re-identification (Re-ID) faces two key
issues. One is the data distribution discrepancy between source and target
domains, and the other is the lack of labelling information in target domain.
They are addressed in this paper from the perspective of representation
learning. For the first issue, we highlight the presence of camera-level
sub-domains as a unique characteristic of person Re-ID, and develop
camera-aware domain adaptation to reduce the discrepancy not only between
source and target domains but also across these sub-domains. For the second
issue, we exploit the temporal continuity in each camera of target domain to
create discriminative information. This is implemented by dynamically
generating online triplets within each batch, in order to maximally take
advantage of the steadily improved feature representation in training process.
Together, the above two methods give rise to a novel unsupervised deep domain
adaptation framework for person Re-ID. Experiments and ablation studies on
benchmark datasets demonstrate its superiority and interesting properties.Comment: Accepted by ICCV201
OPML: A One-Pass Closed-Form Solution for Online Metric Learning
To achieve a low computational cost when performing online metric learning
for large-scale data, we present a one-pass closed-form solution namely OPML in
this paper. Typically, the proposed OPML first adopts a one-pass triplet
construction strategy, which aims to use only a very small number of triplets
to approximate the representation ability of whole original triplets obtained
by batch-manner methods. Then, OPML employs a closed-form solution to update
the metric for new coming samples, which leads to a low space (i.e., )
and time (i.e., ) complexity, where is the feature dimensionality.
In addition, an extension of OPML (namely COPML) is further proposed to enhance
the robustness when in real case the first several samples come from the same
class (i.e., cold start problem). In the experiments, we have systematically
evaluated our methods (OPML and COPML) on three typical tasks, including UCI
data classification, face verification, and abnormal event detection in videos,
which aims to fully evaluate the proposed methods on different sample number,
different feature dimensionalities and different feature extraction ways (i.e.,
hand-crafted and deeply-learned). The results show that OPML and COPML can
obtain the promising performance with a very low computational cost. Also, the
effectiveness of COPML under the cold start setting is experimentally verified.Comment: 12 page
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