• Corpus ID: 232428301

Deep adaptive fuzzy clustering for evolutionary unsupervised representation learning

@article{Tan2021DeepAF,
  title={Deep adaptive fuzzy clustering for evolutionary unsupervised representation learning},
  author={Dayu Tan and Zheng Huang and Xin Peng and Weimin Zhong and Vladimir Mahalec},
  journal={ArXiv},
  year={2021},
  volume={abs/2103.17086}
}
Cluster assignment of large and complex images is a crucial but challenging task in pattern recognition and computer vision. In this study, we explore the possibility of employing fuzzy clustering in a deep neural network framework. Thus, we present a novel evolutionary unsupervised learning representation model with iterative optimization. It implements the deep adaptive fuzzy clustering (DAFC) strategy that learns a convolutional neural network classifier from given only unlabeled data… 

References

SHOWING 1-10 OF 43 REFERENCES
Deep Adaptive Image Clustering
TLDR
Deep Adaptive Clustering (DAC) is proposed that recasts the clustering problem into a binary pairwise-classification framework to judge whether pairs of images belong to the same clusters to overcome the main challenge, the ground-truth similarities are unknown in image clustering.
Deep Clustering with Convolutional Autoencoders
TLDR
A convolutional autoencoders structure is developed to learn embedded features in an end-to-end way and a clustering oriented loss is directly built on embedded features to jointly perform feature refinement and cluster assignment.
Deep Comprehensive Correlation Mining for Image Clustering
TLDR
A novel clustering framework, named deep comprehensive correlation mining~(DCCM), for exploring and taking full advantage of various kinds of correlations behind the unlabeled data from three aspects: Instead of only using pair-wise information, pseudo-label supervision is proposed to investigate category information and learn discriminative features.
Structural Deep Clustering Network
TLDR
A Structural Deep Clustering Network (SDCN) is proposed to integrate the structural information into deep clustering, with a delivery operator to transfer the representations learned by autoencoder to the corresponding GCN layer, and a dual self-supervised mechanism to unify these two different deep neural architectures and guide the update of the whole model.
Deep Self-Evolution Clustering
TLDR
The clustering task is recast as a binary pairwise-classification problem to estimate whether pairwise patterns are similar and an alternating iterative algorithm called Self-Evolution Clustering Training (SECT) is presented to select similar and dissimilar pairwise pattern and to train the DNN alternately.
Deep Clustering for Unsupervised Learning of Visual Features
TLDR
This work presents DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features and outperforms the current state of the art by a significant margin on all the standard benchmarks.
Joint Unsupervised Learning of Deep Representations and Image Clusters
TLDR
A recurrent framework for joint unsupervised learning of deep representations and image clusters by integrating two processes into a single model with a unified weighted triplet loss function and optimizing it end-to-end can obtain not only more powerful representations, but also more precise image clusters.
Online Deep Clustering for Unsupervised Representation Learning
TLDR
This work proposes Online Deep Clustering (ODC) that performs clustering and network update simultaneously rather than alternatingly, and designs and maintains two dynamic memory modules, i.e., samples memory to store samples' labels and features, andCentroids memory for centroids evolution.
Invariant Information Clustering for Unsupervised Image Classification and Segmentation
We present a novel clustering objective that learns a neural network classifier from scratch, given only unlabelled data samples. The model discovers clusters that accurately match semantic classes,
Unsupervised Deep Embedding for Clustering Analysis
TLDR
Deep Embedded Clustering is proposed, a method that simultaneously learns feature representations and cluster assignments using deep neural networks and learns a mapping from the data space to a lower-dimensional feature space in which it iteratively optimizes a clustering objective.
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