Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Visually Analyzing and Steering Zero Shot Learning   We propose a visual analytics system to help a user analyze and steer
zero-shot learning models. Zero-shot learning has emerged as a viable scenario
for categorizing data that consists of no labeled examples, and thus a
promising approach to minimize data annotation from humans. However, it is
challenging to understand where zero-shot learning fails, the cause of such
failures, and how a user can modify the model to prevent such failures. Our
visualization system is designed to help users diagnose and understand
mispredictions in such models, so that they may gain insight on the behavior of
a model when applied to data associated with categories not seen during
training. Through usage scenarios, we highlight how our system can help a user
improve performance in zero-shot learning.

22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin