Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-labelling via simultaneous clustering and representation learning   Combining clustering and representation learning is one of the most promising
approaches for unsupervised learning of deep neural networks. However, doing so
naively leads to ill posed learning problems with degenerate solutions. In this
paper, we propose a novel and principled learning formulation that addresses
these issues. The method is obtained by maximizing the information between
labels and input data indices. We show that this criterion extends standard
crossentropy minimization to an optimal transport problem, which we solve
efficiently for millions of input images and thousands of labels using a fast
variant of the Sinkhorn-Knopp algorithm. The resulting method is able to
self-label visual data so as to train highly competitive image representations
without manual labels. Our method achieves state of the art representation
learning performance for AlexNet and ResNet-50 on SVHN, CIFAR-10, CIFAR-100 and
ImageNet and yields the first self-supervised AlexNet that outperforms the
supervised Pascal VOC detection baseline. Code and models are available.

27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn