Deep Self-Evolution Clustering (English)

In: IEEE Transactions on Pattern Analysis and Machine Intelligence   ;  42 ,  4  ;  809-823  ;  2020

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Clustering is a crucial but challenging task in pattern analysis and machine learning. Existing methods often ignore the combination between representation learning and clustering. To tackle this problem, we reconsider the clustering task from its definition to develop Deep Self-Evolution Clustering (DSEC) to jointly learn representations and cluster data. For this purpose, the clustering task is recast as a binary pairwise-classification problem to estimate whether pairwise patterns are similar. Specifically, similarities between pairwise patterns are defined by the dot product between indicator features which are generated by a deep neural network (DNN). To learn informative representations for clustering, clustering constraints are imposed on the indicator features to represent specific concepts with specific representations. Since the ground-truth similarities are unavailable in clustering, an alternating iterative algorithm called Self-Evolution Clustering Training (SECT) is presented to select similar and dissimilar pairwise patterns and to train the DNN alternately. Consequently, the indicator features tend to be one-hot vectors and the patterns can be clustered by locating the largest response of the learned indicator features. Extensive experiments strongly evidence that DSEC outperforms current models on twelve popular image, text and audio datasets consistently.

Table of contents – Volume 42, Issue 4

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The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.

A Hybrid RNN-HMM Approach for Weakly Supervised Temporal Action Segmentation
Kuehne, Hilde / Richard, Alexander / Gall, Juergen | 2020
Automated Video Face Labelling for Films and TV Material
Parkhi, Omkar M. / Rahtu, Esa / Cao, Qiong / Zisserman, Andrew | 2020
Baselines Extraction from Curved Document Images via Slope Fields Recovery
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Deep Self-Evolution Clustering
Chang, Jianlong / Meng, Gaofeng / Wang, Lingfeng / Xiang, Shiming / Pan, Chunhong | 2020
Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs
Malkov, Yu A. / Yashunin, D. A. | 2020
Extracting Geometric Structures in Images with Delaunay Point Processes
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Group Maximum Differentiation Competition: Model Comparison with Few Samples
Ma, Kede / Duanmu, Zhengfang / Wang, Zhou / Wu, Qingbo / Liu, Wentao / Yong, Hongwei / Li, Hongliang / Zhang, Lei | 2020
Hierarchical Bayesian Inverse Lighting of Portraits with a Virtual Light Stage
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Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI
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Perspective-Adaptive Convolutions for Scene Parsing
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Tensor Robust Principal Component Analysis with a New Tensor Nuclear Norm
Lu, Canyi / Feng, Jiashi / Chen, Yudong / Liu, Wei / Lin, Zhouchen / Yan, Shuicheng | 2020
Tracking-by-Fusion via Gaussian Process Regression Extended to Transfer Learning
Gao, Jin / Wang, Qiang / Xing, Junliang / Ling, Haibin / Hu, Weiming / Maybank, Stephen | 2020
Unsupervised Person Re-Identification by Deep Asymmetric Metric Embedding
Yu, Hong-Xing / Wu, Ancong / Zheng, Wei-Shi | 2020
Visibility Graphs for Image Processing
Iacovacci, Jacopo / Lacasa, Lucas | 2020
Weighted Manifold Alignment using Wave Kernel Signatures for Aligning Medical Image Datasets
Clough, James R. / Balfour, Daniel R. / Cruz, Gastao / Marsden, Paul K. / Prieto, Claudia / Reader, Andrew J. / King, Andrew P. | 2020
Denoising Autoencoders for Overgeneralization in Neural Networks
Spigler, Giacomo | 2020
Efficient Graph Cut Optimization for Full CRFs with Quantized Edges
Veksler, Olga | 2020
Learning Raw Image Reconstruction-Aware Deep Image Compressors
Punnappurath, Abhijith / Brown, Michael S. | 2020
| 2020
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