aiqbench analysis
IdEst introduces a Minimum Spanning Tree-based method to estimate intrinsic dimensionality as a geometric proxy for evaluating self-supervised learning representations. This approach offers a computationally efficient alternative to standard linear probing, enabling faster hyperparameter selection while maintaining strong correlation with downstream performance. The technique provides a principled way to assess representation quality without the high costs and sensitivity associated with traditional supervised protocols.
IdEst: Assessing Self-Supervised Learning Representations via Intrinsic Dimension
arXiv:2606.03338v1 Announce Type: new Abstract: Self-supervised learning (SSL) has emerged as a powerful paradigm for learning meaningful representations from unlabeled data. However, the standard protocol for evaluating these representations, linear probing, is computationally expensive, sensitive to hyperparameters, and provides limited insight into the geometric structure of the representation space. In this work, motivated by connections between neural network generalization and intrinsic dimension (ID) we propose IdEst, a method for estimating the ID of SSL representations via the Minimum Spanning Tree dimension estimator ($\mathrm{dim}_\mathrm{MST}$). Across diverse datasets, architectures, and SSL pretraining objectives, we show that IdEst strongly correlates with downstream linear probe performances. Furthermore, we demonstrate that IdEst enables efficient hyperparameter selection, significantly reducing the computational cost compared to supervised alternatives. Our results highlight intrinsic dimensionality as a principled geometric proxy for assessing SSL representations, complementing standard supervised probing protocols.
Abstract:Self-supervised learning (SSL) has emerged as a powerful paradigm for learning meaningful representations from unlabeled data. However, the standard protocol for evaluating these representations, linear probing, is computationally expensive, sensitive to hyperparameters, and provides limited insight into the geometric structure of the representation space. In this work, motivated by connections between neural network generalization and intrinsic dimension (ID) we propose IdEst, a method for estimating the ID of SSL representations via the Minimum Spanning Tree dimension estimator ($\mathrm{dim}_\mathrm{MST}$). Across diverse datasets, architectures, and SSL pretraining objectives, we show that IdEst strongly correlates with downstream linear probe performances. Furthermore, we demonstrate that IdEst enables efficient hyperparameter selection, significantly reducing the computational cost compared to supervised alternatives. Our results highlight intrinsic dimensionality as a principled geometric proxy for assessing SSL representations, complementing standard supervised probing protocols.
Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2606.03338 [cs.LG]
(or arXiv:2606.03338v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.03338 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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