Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Active Testing: An Efficient and Robust Framework for Estimating
  Accuracy   Much recent work on visual recognition aims to scale up learning to massive,
noisily-annotated datasets. We address the problem of scaling- up the
evaluation of such models to large-scale datasets with noisy labels. Current
protocols for doing so require a human user to either vet (re-annotate) a small
fraction of the test set and ignore the rest, or else correct errors in
annotation as they are found through manual inspection of results. In this
work, we re-formulate the problem as one of active testing, and examine
strategies for efficiently querying a user so as to obtain an accu- rate
performance estimate with minimal vetting. We demonstrate the effectiveness of
our proposed active testing framework on estimating two performance metrics,
Precision@K and mean Average Precision, for two popular computer vision tasks,
multi-label classification and instance segmentation. We further show that our
approach is able to save significant human annotation effort and is more robust
than alternative evaluation protocols.

6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex