Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced
  Classification by Training on Random Noise Images   Despite remarkable progress on visual recognition tasks, deep neural-nets
still struggle to generalize well when training data is scarce or highly
imbalanced, rendering them extremely vulnerable to real-world examples. In this
paper, we present a surprisingly simple yet highly effective method to mitigate
this limitation: using pure noise images as additional training data. Unlike
the common use of additive noise or adversarial noise for data augmentation, we
propose an entirely different perspective by directly training on pure random
noise images. We present a new Distribution-Aware Routing Batch Normalization
layer (DAR-BN), which enables training on pure noise images in addition to
natural images within the same network. This encourages generalization and
suppresses overfitting. Our proposed method significantly improves imbalanced
classification performance, obtaining state-of-the-art results on a large
variety of long-tailed image classification datasets (CIFAR-10-LT,
CIFAR-100-LT, ImageNet-LT, Places-LT, and CelebA-5). Furthermore, our method is
extremely simple and easy to use as a general new augmentation tool (on top of
existing augmentations), and can be incorporated in any training scheme. It
does not require any specialized data generation or training procedures, thus
keeping training fast and efficient

5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con