Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Addressing Feature Suppression in Unsupervised Visual Representations   Contrastive learning is one of the fastest growing research areas in machine
learning due to its ability to learn useful representations without labeled
data. However, contrastive learning is susceptible to feature suppression,
i.e., it may discard important information relevant to the task of interest,
and learn irrelevant features. Past work has addressed this limitation via
handcrafted data augmentations that eliminate irrelevant information. This
approach however does not work across all datasets and tasks. Further, data
augmentations fail in addressing feature suppression in multi-attribute
classification when one attribute can suppress features relevant to other
attributes. In this paper, we analyze the objective function of contrastive
learning and formally prove that it is vulnerable to feature suppression. We
then present predictive contrastive learning (PCL), a framework for learning
unsupervised representations that are robust to feature suppression. The key
idea is to force the learned representation to predict the input, and hence
prevent it from discarding important information. Extensive experiments verify
that PCL is robust to feature suppression and outperforms state-of-the-art
contrastive learning methods on a variety of datasets and tasks.

17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio