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Reconstruction Regularized Deep Metric Learning for Multi-label Image Classification
TLDR
Zhang et al. as mentioned in this paper proposed a two-way deep metric learning method for multi-label image classification, where images and labels are embedded via two unique deep neural networks, respectively, and a reconstruction module is incorporated into the whole framework as a regularization term.Abstract:
In this paper, we present a novel deep metric learning method to tackle the multi-label image classification problem. In order to better learn the correlations among images features, as well as labels, we attempt to explore a latent space, where images and labels are embedded via two unique deep neural networks, respectively. To capture the relationships between image features and labels, we aim to learn a \emph{two-way} deep distance metric over the embedding space from two different views, i.e., the distance between one image and its labels is not only smaller than those distances between the image and its labels' nearest neighbors, but also smaller than the distances between the labels and other images corresponding to the labels' nearest neighbors. Moreover, a reconstruction module for recovering correct labels is incorporated into the whole framework as a regularization term, such that the label embedding space is more representative. Our model can be trained in an end-to-end manner. Experimental results on publicly available image datasets corroborate the efficacy of our method compared with the state-of-the-arts.read more
Citations
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Journal ArticleDOI
Delving Deep into Label Smoothing
TL;DR: An Online Label Smoothing (OLS) strategy is presented, which generates soft labels based on the statistics of the model prediction for the target category, which can significantly improve the robustness of DNN models to noisy labels compared to current label smoothing approaches.
Proceedings ArticleDOI
Comparison of Multi-Label Classification Algorithms for Code Smell Detection
TL;DR: This paper has used machine learning techniques, especially multi-label classification methods, to classify whether the given source code is affected with more than one code smells or not, and shows that Random Forest algorithm performs better than Decision Tree, Naive Bayes, Support Vector Machine and Neural Network algorithms.
Book ChapterDOI
Recurrent Image Annotation with Explicit Inter-label Dependencies
TL;DR: This paper proposes a novel approach in which the RNN is explicitly forced to learn multiple relevant inter-label dependencies, without the need of feeding the ground-truth in any particular order, and outperforms several state-of-the-art techniques on two popular datasets.
Journal Article
Unsupervised anomaly detection via deep metric learning with end-to-end optimization
TL;DR: This work investigates unsupervised anomaly detection for high-dimensional data and introduces a deep metric learning (DML) based framework that learns a distance metric through a deep neural network and employs the hard mining technique from the DML literature.
Journal ArticleDOI
MLCE: A Multi-Label Crotch Ensemble Method for Multi-Label Classification
TL;DR: This paper proposes a multi-label crotch ensemble (MLCE) model for multi- label classification that addresses the problem that each instance is associated with multiple labels simultaneously.
References
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
Journal ArticleDOI
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.