# Clustering based Contrastive Learning for Improving Face Representations

arXiv: https://arxiv.org/abs/2004.02195 · HTML: https://mlsanity.com/paper/2004.02195/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2020.

## Built on by (most cited first, 21 of 21)

- [TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series](https://mlsanity.com/paper/2308.08241/) (2023)
- [MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series](https://mlsanity.com/paper/2212.01141/) (2022)
- [Face, Body, Voice: Video Person-Clustering with Multiple Modalities](https://mlsanity.com/paper/2105.09939/) (2021)
- [SLIC: Self-Supervised Learning with Iterative Clustering for Human Action Videos](https://mlsanity.com/paper/2206.12534/) (2022)
- [Unsupervised Representation Learning for Time Series: A Review](https://mlsanity.com/paper/2308.01578/) (2023)
- [TimeDRL: Disentangled Representation Learning for Multivariate Time-Series](https://mlsanity.com/paper/2312.04142/) (2023)
- [Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding](https://mlsanity.com/paper/2211.03348/) (2022)
- [Unsupervised Local Discrimination for Medical Images](https://mlsanity.com/paper/2108.09440/) (2021)
- [Self-supervised Video-centralised Transformer for Video Face Clustering](https://mlsanity.com/paper/2203.13166/) (2022)
- [Adversarial Contrastive Learning by Permuting Cluster Assignments](https://mlsanity.com/paper/2204.10314/) (2022)
- [Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series](https://mlsanity.com/paper/2410.12606/) (2024)
- [Physically Disentangled Representations](https://mlsanity.com/paper/2204.05281/) (2022)
- [Self-Contained Entity Discovery from Captioned Videos](https://mlsanity.com/paper/2208.06662/) (2022)
- [Cross-view Graph Contrastive Representation Learning on Partially Aligned Multi-view Data](https://mlsanity.com/paper/2211.04906/) (2022)
- [Cluster-aware Contrastive Learning for Unsupervised Out-of-distribution Detection](https://mlsanity.com/paper/2302.02598/) (2023)
- [Open-World Semi-Supervised Learning for Node Classification](https://mlsanity.com/paper/2403.11483/) (2024)
- [VideoClusterNet: Self-Supervised and Adaptive Face Clustering For Videos](https://mlsanity.com/paper/2407.12214/) (2024)
- [Few-Example Clustering via Contrastive Learning](https://mlsanity.com/paper/2207.04050/) (2022)
- [Unified and Dynamic Graph for Temporal Character Grouping in Long Videos](https://mlsanity.com/paper/2308.14105/) (2023)
- [Face Clustering for Connection Discovery from Event Images](https://mlsanity.com/paper/2309.01092/) (2023)
- [Faithful Density-Peaks Clustering via Matrix Computations on MPI Parallelization System](https://mlsanity.com/paper/2406.12297/) (2024)

## Abstract (arXiv)

A good clustering algorithm can discover natural groupings in data. These groupings, if used wisely, provide a form of weak supervision for learning representations. In this work, we present Clustering-based Contrastive Learning (CCL), a new clustering-based representation learning approach that uses labels obtained from clustering along with video constraints to learn discriminative face features. We demonstrate our method on the challenging task of learning representations for video face clustering. Through several ablation studies, we analyze the impact of creating pair-wise positive and negative labels from different sources. Experiments on three challenging video face clustering datasets: BBT-0101, BF-0502, and ACCIO show that CCL achieves a new state-of-the-art on all datasets.