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\title{Quantifying Representation Reliability in Self-Supervised Learning Models}





\author[1]{Young-Jin Park}
\author[2]{Hao Wang}
\author[ ]{Shervin Ardeshir}
\author[1]{Navid Azizan}
\affil[1]{%
    Massachusetts Institute of Technology
}
\affil[2]{%
    MIT-IBM Watson AI Lab
}
  
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\begin{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 \emph{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 \emph{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.
The code is available at \url{https://github.com/azizanlab/repreli}.
\end{abstract}



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\begin{acknowledgements}
The authors would like to thank Dr. Akash Srivastava for insightful discussions.
This work was supported in part by the MIT-IBM Watson AI Lab, MathWorks, and Amazon. The authors also acknowledge the MIT SuperCloud and Lincoln Laboratory Supercomputing Center for providing computing resources that have contributed to the results reported within this paper. %
\end{acknowledgements}


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