Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Big-Little Net: An Efficient Multi-Scale Feature Representation for
  Visual and Speech Recognition   In this paper, we propose a novel Convolutional Neural Network (CNN)
architecture for learning multi-scale feature representations with good
tradeoffs between speed and accuracy. This is achieved by using a multi-branch
network, which has different computational complexity at different branches.
Through frequent merging of features from branches at distinct scales, our
model obtains multi-scale features while using less computation. The proposed
approach demonstrates improvement of model efficiency and performance on both
object recognition and speech recognition tasks,using popular architectures
including ResNet and ResNeXt. For object recognition, our approach reduces
computation by 33% on object recognition while improving accuracy with 0.9%.
Furthermore, our model surpasses state-of-the-art CNN acceleration approaches
by a large margin in accuracy and FLOPs reduction. On the task of speech
recognition, our proposed multi-scale CNNs save 30% FLOPs with slightly better
word error rates, showing good generalization across domains. The codes are
available at https://github.com/IBM/BigLittleNet

16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio