[2312.03213] Bootstrap Your Own Variance
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Machine Learning

arXiv:2312.03213 (cs)
[Submitted on 6 Dec 2023]

Title:Bootstrap Your Own Variance

Authors:Polina Turishcheva, Jason Ramapuram, Sinead Williamson, Dan Busbridge, Eeshan Dhekane, Russ Webb
View PDF HTML (experimental)
Abstract:Understanding model uncertainty is important for many applications. We propose Bootstrap Your Own Variance (BYOV), combining Bootstrap Your Own Latent (BYOL), a negative-free Self-Supervised Learning (SSL) algorithm, with Bayes by Backprop (BBB), a Bayesian method for estimating model posteriors. We find that the learned predictive std of BYOV vs. a supervised BBB model is well captured by a Gaussian distribution, providing preliminary evidence that the learned parameter posterior is useful for label free uncertainty estimation. BYOV improves upon the deterministic BYOL baseline (+2.83% test ECE, +1.03% test Brier) and presents better calibration and reliability when tested with various augmentations (eg: +2.4% test ECE, +1.2% test Brier for Salt & Pepper noise).
Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:2312.03213 [cs.LG]
  (or arXiv:2312.03213v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2312.03213
Journal reference: NeurIPS 2023 Workshop: Self-Supervised Learning - Theory and Practice

Submission history

From: Jason Ramapuram [view email]
[v1] Wed, 6 Dec 2023 01:02:13 UTC (5,924 KB)
Full-text links:

Access Paper:

view license
Current browse context:
cs.LG
< prev   |   next >
new | recent | 2023-12
Change to browse by:
cs
stat
stat.ML

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
IArxiv Recommender (What is IArxiv?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)