Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Meta Approach to Data Augmentation Optimization   Data augmentation policies drastically improve the performance of image
recognition tasks, especially when the policies are optimized for the target
data and tasks. In this paper, we propose to optimize image recognition models
and data augmentation policies simultaneously to improve the performance using
gradient descent. Unlike prior methods, our approach avoids using proxy tasks
or reducing search space, and can directly improve the validation performance.
Our method achieves efficient and scalable training by approximating the
gradient of policies by implicit gradient with Neumann series approximation. We
demonstrate that our approach can improve the performance of various image
classification tasks, including ImageNet classification and fine-grained
recognition, without using dataset-specific hyperparameter tuning.

21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph