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- Learning Efficient Convolutional Networks through Network Slimming2,909
The approach is called network slimming, which takes wide and large networks as input models, but during training insignificant channels are automatically identified and pruned afterwards, yielding thin and compact models with comparable accuracy.
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- Learning a Mahalanobis distance metric for data clustering and classification637
This paper considers a general problem of learning from pairwise constraints in the form of must-links and cannot-links, and aims to learn a Mahalanobis distance metric.
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- An in-field automatic wheat disease diagnosis system394
Experimental results demonstrate that the proposed system outperforms conventional CNN architectures on recognition accuracy under the same amount of parameters, meanwhile maintaining accurate localization for corresponding disease areas.
- Manifold-ranking based image retrieval389
MRBIR first makes use of a manifold ranking algorithm to explore the relationship among all the data points in the feature space, and then measures relevance between the query and all the images in the database accordingly, which is different from traditional similarity metrics based on pair-wise distance.
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- Trace ratio criterion for feature selection378
A novel algorithm is proposed to efficiently find the global optimal feature subset such that the subset-level score is maximized, and extensive experiments demonstrate the effectiveness of the proposed algorithm in comparison with the traditional methods for feature selection.
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- Robust multi-task feature learning315
This paper proposes a Robust Multi-Task Feature Learning algorithm (rMTFL) which simultaneously captures a common set of features among relevant tasks and identifies outlier tasks, and provides a detailed theoretical analysis on the proposed rMTFL formulation.
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- DeepFish: Accurate underwater live fish recognition with a deep architecture258
A framework to recognize fish from videos captured by underwater cameras deployed in the ocean observation network is proposed, using a deep architecture to extract features of the foreground fish images and a linear SVM classifier for classification.
- Learning and Innovative Elements of Strategy Adoption Rules Expand Cooperative Network Topologies223
The results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.
- Semi-supervised Multi-label Learning by Solving a Sylvester Equation214
It is shown that the labels of unlabeled data finally can be obtained by solving a Sylvester Equation, and a regularization framework combining two regularization terms for the two graphs is suggested.
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- Semi-Supervised Clustering via Matrix Factorization194
A novel matrix factorization based approach for semi-supervised clustering, which is extended to co-cluster the data sets of different types with constraints and shows the superiority of the proposed method.
- Current trends in the development of intelligent unmanned autonomous systems182
The trends in the development of intelligent unmanned autonomous systems are introduced by summarizing the main achievements in each technological platform by classify the relevant technologies into seven areas.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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