Quantifying Representation Reliability in Self-Supervised Learning Models

Young-Jin Park, Hao Wang, Shervin Ardeshir, Navid Azizan
Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, PMLR 244:2835-2860, 2024.

Abstract

Self-supervised learning models extract general-purpose representations from data. Quantifying the reliability of these representations is crucial, as many downstream models rely on them as input for their own tasks. To this end, we introduce a formal definition of _representation reliability_: the representation for a given test point is considered to be reliable if the downstream models built on top of that representation can consistently generate accurate predictions for that test point. However, accessing downstream data to quantify the representation reliability is often infeasible or restricted due to privacy concerns. We propose an ensemble-based method for estimating the representation reliability without knowing the downstream tasks a priori. Our method is based on the concept of _neighborhood consistency_ across distinct pre-trained representation spaces. The key insight is to find shared neighboring points as anchors to align these representation spaces before comparing them. We demonstrate through comprehensive numerical experiments that our method effectively captures the representation reliability with a high degree of correlation, achieving robust and favorable performance compared with baseline methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v244-park24a, title = {Quantifying Representation Reliability in Self-Supervised Learning Models}, author = {Park, Young-Jin and Wang, Hao and Ardeshir, Shervin and Azizan, Navid}, booktitle = {Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence}, pages = {2835--2860}, year = {2024}, editor = {Kiyavash, Negar and Mooij, Joris M.}, volume = {244}, series = {Proceedings of Machine Learning Research}, month = {15--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v244/main/assets/park24a/park24a.pdf}, url = {https://proceedings.mlr.press/v244/park24a.html}, abstract = {Self-supervised learning models extract general-purpose representations from data. Quantifying the reliability of these representations is crucial, as many downstream models rely on them as input for their own tasks. To this end, we introduce a formal definition of _representation reliability_: the representation for a given test point is considered to be reliable if the downstream models built on top of that representation can consistently generate accurate predictions for that test point. However, accessing downstream data to quantify the representation reliability is often infeasible or restricted due to privacy concerns. We propose an ensemble-based method for estimating the representation reliability without knowing the downstream tasks a priori. Our method is based on the concept of _neighborhood consistency_ across distinct pre-trained representation spaces. The key insight is to find shared neighboring points as anchors to align these representation spaces before comparing them. We demonstrate through comprehensive numerical experiments that our method effectively captures the representation reliability with a high degree of correlation, achieving robust and favorable performance compared with baseline methods.} }
Endnote
%0 Conference Paper %T Quantifying Representation Reliability in Self-Supervised Learning Models %A Young-Jin Park %A Hao Wang %A Shervin Ardeshir %A Navid Azizan %B Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2024 %E Negar Kiyavash %E Joris M. Mooij %F pmlr-v244-park24a %I PMLR %P 2835--2860 %U https://proceedings.mlr.press/v244/park24a.html %V 244 %X Self-supervised learning models extract general-purpose representations from data. Quantifying the reliability of these representations is crucial, as many downstream models rely on them as input for their own tasks. To this end, we introduce a formal definition of _representation reliability_: the representation for a given test point is considered to be reliable if the downstream models built on top of that representation can consistently generate accurate predictions for that test point. However, accessing downstream data to quantify the representation reliability is often infeasible or restricted due to privacy concerns. We propose an ensemble-based method for estimating the representation reliability without knowing the downstream tasks a priori. Our method is based on the concept of _neighborhood consistency_ across distinct pre-trained representation spaces. The key insight is to find shared neighboring points as anchors to align these representation spaces before comparing them. We demonstrate through comprehensive numerical experiments that our method effectively captures the representation reliability with a high degree of correlation, achieving robust and favorable performance compared with baseline methods.
APA
Park, Y., Wang, H., Ardeshir, S. & Azizan, N.. (2024). Quantifying Representation Reliability in Self-Supervised Learning Models. Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 244:2835-2860 Available from https://proceedings.mlr.press/v244/park24a.html.

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