Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural
  Architecture Search   In this paper, we propose an efficient NAS algorithm for generating
task-specific models that are competitive under multiple competing objectives.
It comprises of two surrogates, one at the architecture level to improve sample
efficiency and one at the weights level, through a supernet, to improve
gradient descent training efficiency. On standard benchmark datasets (C10,
C100, ImageNet), the resulting models, dubbed NSGANetV2, either match or
outperform models from existing approaches with the search being orders of
magnitude more sample efficient. Furthermore, we demonstrate the effectiveness
and versatility of the proposed method on six diverse non-standard datasets,
e.g. STL-10, Flowers102, Oxford Pets, FGVC Aircrafts etc. In all cases,
NSGANetV2s improve the state-of-the-art (under mobile setting), suggesting that
NAS can be a viable alternative to conventional transfer learning approaches in
handling diverse scenarios such as small-scale or fine-grained datasets. Code
is available at https://github.com/mikelzc1990/nsganetv2

7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
15
The categories of this paper are: nucl-ex