Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sketch-a-Net that Beats Humans   We propose a multi-scale multi-channel deep neural network framework that,
for the first time, yields sketch recognition performance surpassing that of
humans. Our superior performance is a result of explicitly embedding the unique
characteristics of sketches in our model: (i) a network architecture designed
for sketch rather than natural photo statistics, (ii) a multi-channel
generalisation that encodes sequential ordering in the sketching process, and
(iii) a multi-scale network ensemble with joint Bayesian fusion that accounts
for the different levels of abstraction exhibited in free-hand sketches. We
show that state-of-the-art deep networks specifically engineered for photos of
natural objects fail to perform well on sketch recognition, regardless whether
they are trained using photo or sketch. Our network on the other hand not only
delivers the best performance on the largest human sketch dataset to date, but
also is small in size making efficient training possible using just CPUs.

25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th