Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CliqueCNN: Deep Unsupervised Exemplar Learning   Exemplar learning is a powerful paradigm for discovering visual similarities
in an unsupervised manner. In this context, however, the recent breakthrough in
deep learning could not yet unfold its full potential. With only a single
positive sample, a great imbalance between one positive and many negatives, and
unreliable relationships between most samples, training of Convolutional Neural
networks is impaired. Given weak estimates of local distance we propose a
single optimization problem to extract batches of samples with mutually
consistent relations. Conflicting relations are distributed over different
batches and similar samples are grouped into compact cliques. Learning exemplar
similarities is framed as a sequence of clique categorization tasks. The CNN
then consolidates transitivity relations within and between cliques and learns
a single representation for all samples without the need for labels. The
proposed unsupervised approach has shown competitive performance on detailed
posture analysis and object classification.

19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas