Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Supervised Representation Learning as Multimodal Variational
  Inference   This paper proposes a probabilistic extension of SimSiam, a recent
self-supervised learning (SSL) method. SimSiam trains a model by maximizing the
similarity between image representations of different augmented views of the
same image. Although uncertainty-aware machine learning has been getting
general like deep variational inference, SimSiam and other SSL are
insufficiently uncertainty-aware, which could lead to limitations on its
potential. The proposed extension is to make SimSiam uncertainty-aware based on
variational inference. Our main contributions are twofold: Firstly, we clarify
the theoretical relationship between non-contrastive SSL and multimodal
variational inference. Secondly, we introduce a novel SSL called variational
inference SimSiam (VI-SimSiam), which incorporates the uncertainty by involving
spherical posterior distributions. Our experiment shows that VI-SimSiam
outperforms SimSiam in classification tasks in ImageNette and ImageWoof by
successfully estimating the representation uncertainty.

5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org