A Probabilistic Approach to Self-Supervised Learning using Cyclical
Stochastic Gradient MCMC
- URL: http://arxiv.org/abs/2308.01271v1
- Date: Wed, 2 Aug 2023 16:52:56 GMT
- Title: A Probabilistic Approach to Self-Supervised Learning using Cyclical
Stochastic Gradient MCMC
- Authors: Masoumeh Javanbakhat, Christoph Lippert
- Abstract summary: We present a practical self-supervised learning method with Cyclical Gradient Hamiltonian Monte Carlo (cSGHMC)
Within this framework, we place a prior over the parameters of a self-supervised learning model and use cSGHMC to approximate the high dimensional and multimodal posterior distribution over the embeddings.
We provide experimental results on multiple classification tasks on four challenging datasets.
- Score: 8.027994148508844
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper we present a practical Bayesian self-supervised learning method
with Cyclical Stochastic Gradient Hamiltonian Monte Carlo (cSGHMC). Within this
framework, we place a prior over the parameters of a self-supervised learning
model and use cSGHMC to approximate the high dimensional and multimodal
posterior distribution over the embeddings. By exploring an expressive
posterior over the embeddings, Bayesian self-supervised learning produces
interpretable and diverse representations. Marginalizing over these
representations yields a significant gain in performance, calibration and
out-of-distribution detection on a variety of downstream classification tasks.
We provide experimental results on multiple classification tasks on four
challenging datasets. Moreover, we demonstrate the effectiveness of the
proposed method in out-of-distribution detection using the SVHN and CIFAR-10
datasets.
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