# AugDMC: Data Augmentation Guided Deep Multiple Clustering

arXiv: https://arxiv.org/abs/2306.13023 · HTML: https://mlsanity.com/paper/2306.13023/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 55 references that are in the map.
- First posted on arXiv 22 Jun 2023.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [Generative Adversarial Network in Medical Imaging: A Review](https://mlsanity.com/paper/1809.07294/) (2018)
- [Learning Data Augmentation Strategies for Object Detection](https://mlsanity.com/paper/1906.11172/) (2019)
- [Data augmentation using learned transformations for one-shot medical image segmentation](https://mlsanity.com/paper/1902.09383/) (2019)
- [Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization](https://mlsanity.com/paper/1704.06327/) (2017)
- [Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis](https://mlsanity.com/paper/1703.03702/) (2017)
- [Improving Image Clustering With Multiple Pretrained CNN Feature Extractors](https://mlsanity.com/paper/1807.07760/) (2018)
- [Multi-View Multiple Clusterings using Deep Matrix Factorization](https://mlsanity.com/paper/1911.11396/) (2019)
- [Multi-View Multiple Clustering](https://mlsanity.com/paper/1905.05053/) (2019)

## Built on by (most cited first, 15 of 15)

- [No Representation Rules Them All in Category Discovery](https://mlsanity.com/paper/2311.17055/) (2023)
- [Multi-Modal Proxy Learning Towards Personalized Visual Multiple Clustering](https://mlsanity.com/paper/2404.15655/) (2024)
- [Organizing Unstructured Image Collections using Natural Language](https://mlsanity.com/paper/2410.05217/) (2024)
- [Conditional Representation Learning for Customized Tasks](https://mlsanity.com/paper/2510.04564/) (2025)
- [Dual-disentangled Deep Multiple Clustering](https://mlsanity.com/paper/2402.05310/) (2024)
- [Open Ad-hoc Categorization with Contextualized Feature Learning](https://mlsanity.com/paper/2512.16202/) (2025)
- [CubeRobot: Grounding Language in Rubik's Cube Manipulation via Vision-Language Model](https://mlsanity.com/paper/2503.19281/) (2025)
- [Text-Guided Alternative Image Clustering](https://mlsanity.com/paper/2406.18589/) (2024)
- [Agent-Centric Personalized Multiple Clustering with Multi-Modal LLMs](https://mlsanity.com/paper/2503.22241/) (2025)
- [ESMC: MLLM-Based Embedding Selection for Explainable Multiple Clustering](https://mlsanity.com/paper/2512.00725/) (2025)
- [Interpretable Text-Guided Image Clustering via Iterative Search](https://mlsanity.com/paper/2506.12514/) (2025)
- [Multi-modal Dynamic Proxy Learning for Personalized Multiple Clustering](https://mlsanity.com/paper/2511.07274/) (2025)
- [Semantic Purification for Conditional Representation Learning](https://mlsanity.com/paper/2602.05464/) (2026)
- [Universal Guideline-Driven Image Clustering via a Hybrid LLM Agent](https://mlsanity.com/paper/2606.24094/) (2026)
- [Controlling Embedding Spaces with Text-Conditioned Transformations](https://mlsanity.com/paper/2607.22919/) (2026)

## Abstract (arXiv)

Clustering aims to group similar objects together while separating dissimilar ones apart. Thereafter, structures hidden in data can be identified to help understand data in an unsupervised manner. Traditional clustering methods such as k-means provide only a single clustering for one data set. Deep clustering methods such as auto-encoder based clustering methods have shown a better performance, but still provide a single clustering. However, a given dataset might have multiple clustering structures and each represents a unique perspective of the data. Therefore, some multiple clustering methods have been developed to discover multiple independent structures hidden in data. Although deep multiple clustering methods provide better performance, how to efficiently capture the alternative perspectives in data is still a problem. In this paper, we propose AugDMC, a novel data Augmentation guided Deep Multiple Clustering method, to tackle the challenge. Specifically, AugDMC leverages data augmentations to automatically extract features related to a certain aspect of the data using a self-supervised prototype-based representation learning, where different aspects of the data can be preserved under different data augmentations. Moreover, a stable optimization strategy is proposed to alleviate the unstable problem from different augmentations. Thereafter, multiple clusterings based on different aspects of the data can be obtained. Experimental results on three real-world datasets compared with state-of-the-art methods validate the effectiveness of the proposed method.