Deep image clustering approaches typically use autoencoder
architectures to learn compressed latent representations suitable for clustering tasks. However, they do not effectively regulate the latent space
during training, leading to low performance and diminished applicability
to different datasets. In this paper, we propose a deep clustering model
combining maximum mean discrepancy (MMD) regularization and self-learning clustering optimization to mitigate this problem. Specifically,
we first train the network to improve its image reconstruction ability
by minimizing both reconstruction loss and MMD divergence from a
target distribution. Then, the model gradually learns from its own high-confidence predictions to further optimize the latent distribution. We
validate the network’s performance on different benchmark image sets
using standard clustering metrics, without changing network configuration or adjusting hyper-parameters between datasets. The proposed
model provides top clustering performance across datasets while being
more robust than state-of-the-art methods.

Deep Image Clustering Using Self-Learning Optimization in a Variational Auto-Encoder

technical paper

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
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The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


Farida Cheriet

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Deep Image Clustering Using Self-Learning Optimization in a Variational Auto-Encoder

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