# Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders

arXiv: https://arxiv.org/abs/1703.07980 · HTML: https://mlsanity.com/paper/1703.07980/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2017.

## Built on by (most cited first, 30 of 32)

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- [Mixture-of-Experts Variational Autoencoder for Clustering and Generating from Similarity-Based Representations on Single Cell Data](https://mlsanity.com/paper/1910.07763/) (2019)
- [ConstraintMatch for Semi-constrained Clustering](https://mlsanity.com/paper/2311.15395/) (2023)
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- [DMS: Differentiable Mean Shift for Dataset Agnostic Task Specific Clustering Using Side Information](https://mlsanity.com/paper/2305.18492/) (2023)
- [Dual Advancement of Representation Learning and Clustering for Sparse and Noisy Images](https://mlsanity.com/paper/2409.01781/) (2024)
- [Enhancing Interpretability in Deep Reinforcement Learning through Semantic Clustering](https://mlsanity.com/paper/2409.17411/) (2024)
- [Autoencoded UMAP-Enhanced Clustering for Unsupervised Learning](https://mlsanity.com/paper/2501.07729/) (2025)
- [fMBN-E: Efficient Unsupervised Network Structure Ensemble and Selection for Clustering](https://mlsanity.com/paper/2107.02071/) (2021)
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- [Deep Embedded Clustering with Distribution Consistency Preservation for Attributed Networks](https://mlsanity.com/paper/2205.14303/) (2022)
- [Deep Multi-View Semi-Supervised Clustering with Sample Pairwise Constraints](https://mlsanity.com/paper/2206.04949/) (2022)
- [Careful Seeding for k-Medois Clustering with Incremental k-Means++ Initialization](https://mlsanity.com/paper/2207.02404/) (2022)
- [SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes](https://mlsanity.com/paper/2208.08879/) (2022)
- [A Saliency-based Clustering Framework for Identifying Aberrant Predictions](https://mlsanity.com/paper/2311.06454/) (2023)
- [A Personalized Video-Based Hand Taxonomy: Application for Individuals with Spinal Cord Injury](https://mlsanity.com/paper/2403.18094/) (2024)
- [Deep Clustering via Distribution Learning](https://mlsanity.com/paper/2408.03407/) (2024)
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## Abstract (arXiv)

Traditional image clustering methods take a two-step approach, feature learning and clustering, sequentially. However, recent research results demonstrated that combining the separated phases in a unified framework and training them jointly can achieve a better performance. In this paper, we first introduce fully convolutional auto-encoders for image feature learning and then propose a unified clustering framework to learn image representations and cluster centers jointly based on a fully convolutional auto-encoder and soft k-means scores. At initial stages of the learning procedure, the representations extracted from the auto-encoder may not be very discriminative for latter clustering. We address this issue by adopting a boosted discriminative distribution, where high score assignments are highlighted and low score ones are de-emphasized. With the gradually boosted discrimination, clustering assignment scores are discriminated and cluster purities are enlarged. Experiments on several vision benchmark datasets show that our methods can achieve a state-of-the-art performance.