Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Abstract Reasoning for Raven's Problem Matrices   Raven's Progressive Matrices (RPM) is highly correlated with human
intelligence, and it has been widely used to measure the abstract reasoning
ability of humans. In this paper, to study the abstract reasoning capability of
deep neural networks, we propose the first unsupervised learning method for
solving RPM problems. Since the ground truth labels are not allowed, we design
a pseudo target based on the prior constraints of the RPM formulation to
approximate the ground truth label, which effectively converts the unsupervised
learning strategy into a supervised one. However, the correct answer is wrongly
labelled by the pseudo target, and thus the noisy contrast will lead to
inaccurate model training. To alleviate this issue, we propose to improve the
model performance with negative answers. Moreover, we develop a
decentralization method to adapt the feature representation to different RPM
problems. Extensive experiments on three datasets demonstrate that our method
even outperforms some of the supervised approaches. Our code is available at
https://github.com/visiontao/ncd.

28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th