Deep Clustering with Convolutional Autoencoders

By Xifeng Guo, Xinwang Liu, En Zhu, Jianping Yin. International Conference on Neural Information Processing, 2017

538 citations recorded by the source corpus.

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Cancer Diagnosis Using Deep Learning: A Bibliographic Review (2019, background): “Convolutional autoencoders learn features from the unlabeled images by using end-to-end learning scheme [132].”
Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review (2020, methodology): “For instance, without any labelled data, the work in [145], being inspired by [146], proposes an unsupervised deep learning method for HSI segmentation that initially exploits 3D convolutional Auto-Encoders (AE) (Figure 13) to learn embedded features, and uses the learnt representations in a cluster”
Contrastive Clustering (2021, methodology): “To solve the problem, deep clustering [10], [13] utilizes neural networks to extract representative information from images for facilitating the downstream clustering tasks.”
Attributed Graph Clustering: A Deep Attentional Embedding Approach (2019, methodology): “There have since then been increasing algorithms based on such deep clustering framework [ Dizaji et al. , 2017; Guo et al. , 2017b ] .”