Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Generation of Training Examples: Applications to Moving
  Vehicle License Plate Recognition   Generative Adversarial Networks (GAN) have attracted much research attention
recently, leading to impressive results for natural image generation. However,
to date little success was observed in using GAN generated images for improving
classification tasks. Here we attempt to explore, in the context of car license
plate recognition, whether it is possible to generate synthetic training data
using GAN to improve recognition accuracy. With a carefully-designed pipeline,
we show that the answer is affirmative. First, a large-scale image set is
generated using the generator of GAN, without manual annotation. Then, these
images are fed to a deep convolutional neural network (DCNN) followed by a
bidirectional recurrent neural network (BRNN) with long short-term memory
(LSTM), which performs the feature learning and sequence labelling. Finally,
the pre-trained model is fine-tuned on real images. Our experimental results on
a few data sets demonstrate the effectiveness of using GAN images: an
improvement of 7.5% over a strong baseline with moderate-sized real data being
available. We show that the proposed framework achieves competitive recognition
accuracy on challenging test datasets. We also leverage the depthwise separate
convolution to construct a lightweight convolutional RNN, which is about half
size and 2x faster on CPU. Combining this framework and the proposed pipeline,
we make progress in performing accurate recognition on mobile and embedded
devices.

10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga