Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Max-margin Class Imbalanced Learning with Gaussian Affinity   Real-world object classes appear in imbalanced ratios. This poses a
significant challenge for classifiers which get biased towards frequent
classes. We hypothesize that improving the generalization capability of a
classifier should improve learning on imbalanced datasets. Here, we introduce
the first hybrid loss function that jointly performs classification and
clustering in a single formulation. Our approach is based on an `affinity
measure' in Euclidean space that leads to the following benefits: (1) direct
enforcement of maximum margin constraints on classification boundaries, (2) a
tractable way to ensure uniformly spaced and equidistant cluster centers, (3)
flexibility to learn multiple class prototypes to support diversity and
discriminability in feature space. Our extensive experiments demonstrate the
significant performance improvements on visual classification and verification
tasks on multiple imbalanced datasets. The proposed loss can easily be plugged
in any deep architecture as a differentiable block and demonstrates robustness
against different levels of data imbalance and corrupted labels.

21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio