Probabilistic Contrastive Learning Recovers the Correct Aleatoric
Uncertainty of Ambiguous Inputs
- URL: http://arxiv.org/abs/2302.02865v3
- Date: Wed, 17 May 2023 14:33:27 GMT
- Title: Probabilistic Contrastive Learning Recovers the Correct Aleatoric
Uncertainty of Ambiguous Inputs
- Authors: Michael Kirchhof, Enkelejda Kasneci, Seong Joon Oh
- Abstract summary: Contrastively trained encoders have recently been proven to invert the data-generating process.
We extend the common InfoNCE objective and encoders to predict latent distributions instead of points.
- Score: 21.38099300190815
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Contrastively trained encoders have recently been proven to invert the
data-generating process: they encode each input, e.g., an image, into the true
latent vector that generated the image (Zimmermann et al., 2021). However,
real-world observations often have inherent ambiguities. For instance, images
may be blurred or only show a 2D view of a 3D object, so multiple latents could
have generated them. This makes the true posterior for the latent vector
probabilistic with heteroscedastic uncertainty. In this setup, we extend the
common InfoNCE objective and encoders to predict latent distributions instead
of points. We prove that these distributions recover the correct posteriors of
the data-generating process, including its level of aleatoric uncertainty, up
to a rotation of the latent space. In addition to providing calibrated
uncertainty estimates, these posteriors allow the computation of credible
intervals in image retrieval. They comprise images with the same latent as a
given query, subject to its uncertainty. Code is available at
https://github.com/mkirchhof/Probabilistic_Contrastive_Learning
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