Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast Recurrent Fully Convolutional Networks for Direct Perception in
  Autonomous Driving   Deep convolutional neural networks (CNNs) have been shown to perform
extremely well at a variety of tasks including subtasks of autonomous driving
such as image segmentation and object classification. However, networks
designed for these tasks typically require vast quantities of training data and
long training periods to converge. We investigate the design rationale behind
end-to-end driving network designs by proposing and comparing three small and
computationally inexpensive deep end-to-end neural network models that generate
driving control signals directly from input images. In contrast to prior work
that segments the autonomous driving task, our models take on a novel approach
to the autonomous driving problem by utilizing deep and thin Fully
Convolutional Nets (FCNs) with recurrent neural nets and low parameter counts
to tackle a complex end-to-end regression task predicting both steering and
acceleration commands. In addition, we include layers optimized for
classification to allow the networks to implicitly learn image semantics. We
show that the resulting networks use 3x fewer parameters than the most recent
comparable end-to-end driving network and 500x fewer parameters than the
AlexNet variations and converge both faster and to lower losses while
maintaining robustness against overfitting.

16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess