743 research outputs found
Unsupervised Triplet Hashing for Fast Image Retrieval
Hashing has played a pivotal role in large-scale image retrieval. With the
development of Convolutional Neural Network (CNN), hashing learning has shown
great promise. But existing methods are mostly tuned for classification, which
are not optimized for retrieval tasks, especially for instance-level retrieval.
In this study, we propose a novel hashing method for large-scale image
retrieval. Considering the difficulty in obtaining labeled datasets for image
retrieval task in large scale, we propose a novel CNN-based unsupervised
hashing method, namely Unsupervised Triplet Hashing (UTH). The unsupervised
hashing network is designed under the following three principles: 1) more
discriminative representations for image retrieval; 2) minimum quantization
loss between the original real-valued feature descriptors and the learned hash
codes; 3) maximum information entropy for the learned hash codes. Extensive
experiments on CIFAR-10, MNIST and In-shop datasets have shown that UTH
outperforms several state-of-the-art unsupervised hashing methods in terms of
retrieval accuracy
ANALYSIS OF PSYCHOLOGICAL PHENOMENON AND ITS DEVELOPMENT AND CHANGE LAW IN ANIMATION ART DESIGN ACTIVITIES
Fence-sitters Protect Cooperation in Complex Networks
Evolutionary game theory is one of the key paradigms behind many scientific
disciplines from science to engineering. In complex networks, because of the
difficulty of formulating the replicator dynamics, most of previous studies are
confined to a numerical level. In this paper, we introduce a vectorial
formulation to derive three classes of individuals' payoff analytically. The
three classes are pure cooperators, pure defectors, and fence-sitters. Here,
fence-sitters are the individuals who change their strategies at least once in
the strategy evolutionary process. As a general approach, our vectorial
formalization can be applied to all the two-strategies games. To clarify the
function of the fence-sitters, we define a parameter, payoff memory, as the
number of rounds that the individuals' payoffs are aggregated. We observe that
the payoff memory can control the fence-sitters' effects and the level of
cooperation efficiently. Our results indicate that the fence-sitters' role is
nontrivial in the complex topologies, which protects cooperation in an indirect
way. Our results may provide a better understanding of the composition of
cooperators in a circumstance where the temptation to defect is larger.Comment: an article with 6 pages, 3 figure
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