2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-8.9
Paper Title IDECF: IMPROVED DEEP EMBEDDING CLUSTERING WITH DEEP FUZZY SUPERVISION
Authors Mohammadreza Sadeghi, Narges Armanfard, McGill University / Mila-Quebec AI Institute, Canada
SessionMLR-APPL-IP-8: Machine learning for image processing 8
LocationArea E
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
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Abstract Deep clustering algorithms utilize a deep neural network to map data points in a lower-dimensional space which is more suitable for clustering task. Recent algorithms employ autoencoder to jointly learn a lower-dimensional space (aka latent space) and perform data clustering through minimizing a clustering loss. These algorithms suffer from the fact that the true cluster assignments are unknown because of the unsupervised nature of the task. Thus, they adopt a self-training strategy and estimate the true cluster labels using the algorithm parameters; while the true parameters' value is unknown at the problem outset. To address this difficulty, we propose a deep clustering technique, called IDECF, whereby the true cluster assignments are estimated using an individual deep fully connected network (FCM-Net) which takes its input from the latent space of an autoencoder. The proposed IDECF is trained in an end-to-end manner by minimizing a linear combination of reconstruction loss and clustering loss. Experimental results on benchmark datasets demonstrate the viability and effectiveness of the proposed algorithm.