# Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learning

arXiv: https://arxiv.org/abs/2507.14137 · HTML: https://mlsanity.com/paper/2507.14137/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 81 of its 94 references that are in the map.
- First posted on arXiv 18 Jul 2025.

## Builds on (most cited first, 30 of 81)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Natural Adversarial Examples](https://mlsanity.com/paper/1907.07174/) (2019)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features](https://mlsanity.com/paper/2502.14786/) (2025)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)

## Built on by (most cited first, 22 of 22)

- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [TIPSv2: Advancing Vision-Language Pretraining with Enhanced Patch-Text Alignment](https://mlsanity.com/paper/2604.12012/) (2026)
- [Pathryoshka: Compressing Pathology Foundation Models via Multi-Teacher Knowledge Distillation with Nested Embeddings](https://mlsanity.com/paper/2511.23204/) (2025)
- [From Vicious to Virtuous Cycles: Synergistic Representation Learning for Unsupervised Video Object-Centric Learning](https://mlsanity.com/paper/2602.03390/) (2026)
- [Rethinking Concept Bottleneck Models: From Pitfalls to Solutions](https://mlsanity.com/paper/2603.05629/) (2026)
- [Self-Distillation of Hidden Layers for Self-Supervised Representation Learning](https://mlsanity.com/paper/2603.15553/) (2026)
- [T-REGS: Minimum Spanning Tree Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2510.23484/) (2025)
- [Trajectory Forcing: Structure-First Generation with Controllable Semantic Trajectories](https://mlsanity.com/paper/2606.22527/) (2026)
- [TomoGraphView: 3D Medical Image Classification with Omnidirectional Slice Representations and Graph Neural Networks](https://mlsanity.com/paper/2511.09605/) (2025)
- [MeFEm: Medical Face Embedding model](https://mlsanity.com/paper/2602.14672/) (2026)
- [Representations Before Pixels: Semantics-Guided Hierarchical Video Prediction](https://mlsanity.com/paper/2604.11707/) (2026)
- [Boosting Visual Instruction Tuning with Self-Supervised Guidance](https://mlsanity.com/paper/2604.12966/) (2026)
- [Scene-Centric Unsupervised Video Panoptic Segmentation](https://mlsanity.com/paper/2606.04925/) (2026)
- [Why Prototypes Collapse: Diagnosing and Preventing Partial Collapse in Prototypical Self-Supervised Learning](https://mlsanity.com/paper/2510.20108/) (2025)
- [Suppressing Non-Semantic Noise in Masked Image Modeling Representations](https://mlsanity.com/paper/2604.00172/) (2026)
- [Coevolving Representations in Joint Image-Feature Diffusion](https://mlsanity.com/paper/2604.17492/) (2026)
- [Modular Multimodal Classification Without Fine-Tuning: A Simple Compositional Approach](https://mlsanity.com/paper/2605.20674/) (2026)
- [Learning from Semantic Dictionaries: Discriminative Codebook Contrastive Learning for Unified Visual Representation and Generation](https://mlsanity.com/paper/2605.25012/) (2026)
- [SOCO: Benchmarking Semantic Object Correspondence in Vision Foundation Models](https://mlsanity.com/paper/2605.31597/) (2026)
- [Streaming Neural Speech Codecs through Time-Invariant Representations](https://mlsanity.com/paper/2607.05250/) (2026)
- [Controlling Embedding Spaces with Text-Conditioned Transformations](https://mlsanity.com/paper/2607.22919/) (2026)
- [MC-PanDA++: Simpler, Stronger, and More Robust Domain-Adaptive Panoptic Segmentation](https://mlsanity.com/paper/2609.39681/) (2026)

## Abstract (arXiv)

We present Franca (pronounced Fran-ka): free one; the first fully open-source (data, code, weights) vision foundation model that matches and in many cases surpasses the performance of state-of-the-art proprietary models, e.g., DINOv2, CLIP, SigLIPv2, etc. Our approach is grounded in a transparent training pipeline inspired by Web-SSL and uses publicly available data: ImageNet-21K and a subset of ReLAION-2B. Beyond model release, we tackle critical limitations in SSL clustering methods. While modern models rely on assigning image features to large codebooks via clustering algorithms like Sinkhorn-Knopp, they fail to account for the inherent ambiguity in clustering semantics. To address this, we introduce a parameter-efficient, multi-head clustering projector based on nested Matryoshka representations. This design progressively refines features into increasingly fine-grained clusters without increasing the model size, enabling both performance and memory efficiency. Additionally, we propose a novel positional disentanglement strategy that explicitly removes positional biases from dense representations, thereby improving the encoding of semantic content. This leads to consistent gains on several downstream benchmarks, demonstrating the utility of cleaner feature spaces. Our contributions establish a new standard for transparent, high-performance vision models and open a path toward more reproducible and generalizable foundation models for the broader AI community. The code and model checkpoints are available at https://github.com/valeoai/Franca.