Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Supervised Learning for Large-Scale Unsupervised Image Clustering   Unsupervised learning has always been appealing to machine learning
researchers and practitioners, allowing them to avoid an expensive and
complicated process of labeling the data. However, unsupervised learning of
complex data is challenging, and even the best approaches show much weaker
performance than their supervised counterparts. Self-supervised deep learning
has become a strong instrument for representation learning in computer vision.
However, those methods have not been evaluated in a fully unsupervised setting.
In this paper, we propose a simple scheme for unsupervised classification based
on self-supervised representations. We evaluate the proposed approach with
several recent self-supervised methods showing that it achieves competitive
results for ImageNet classification (39% accuracy on ImageNet with 1000
clusters and 46% with overclustering). We suggest adding the unsupervised
evaluation to a set of standard benchmarks for self-supervised learning. The
code is available at https://github.com/Randl/kmeans_selfsuper

28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio