Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Observations on K-image Expansion of Image-Mixing Augmentation for
  Classification   Image-mixing augmentations (e.g., Mixup or CutMix), which typically mix two
images, have become de-facto training tricks for image classification. Despite
their huge success on image classification, the number of images to mix has not
been profoundly investigated by the previous works, only showing the naive
K-image expansion leads to poor performance degradation. This paper derives a
new K-image mixing augmentation based on the stick-breaking process under
Dirichlet prior. We show that our method can train more robust and generalized
classifiers through extensive experiments and analysis on classification
accuracy, a shape of a loss landscape and adversarial robustness, than the
usual two-image methods. Furthermore, we show that our probabilistic model can
measure the sample-wise uncertainty and can boost the efficiency for Network
Architecture Search (NAS) with 7x reduced search time.

2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn