Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semantically Aligned Bias Reducing Zero Shot Learning   Zero shot learning (ZSL) aims to recognize unseen classes by exploiting
semantic relationships between seen and unseen classes. Two major problems
faced by ZSL algorithms are the hubness problem and the bias towards the seen
classes. Existing ZSL methods focus on only one of these problems in the
conventional and generalized ZSL setting. In this work, we propose a novel
approach, Semantically Aligned Bias Reducing (SABR) ZSL, which focuses on
solving both the problems. It overcomes the hubness problem by learning a
latent space that preserves the semantic relationship between the labels while
encoding the discriminating information about the classes. Further, we also
propose ways to reduce the bias of the seen classes through a simple
cross-validation process in the inductive setting and a novel weak transfer
constraint in the transductive setting. Extensive experiments on three
benchmark datasets suggest that the proposed model significantly outperforms
existing state-of-the-art algorithms by ~1.5-9% in the conventional ZSL setting
and by ~2-14% in the generalized ZSL for both the inductive and transductive
settings.

19
The categories of this paper are: eess
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th