Can Local Binary Convolutions Make Neural Networks
Models Smaller?
Experiment & Results

Background

I We test those models on ImageNet dataset.
Table 1: Accuracy of Models on ImageNet

Model
Top1 Top5 Learned Para.
DenseNet-121 75.46 92.74
6.8M
Full
69.73 89.21
4.7M
Fused
73.70 91.63
5.8M

Figure 1: LBCNN construction.

I Local Binary Convolution Neural Networks(LBCNN) aremodels that use Local Binary Convolution(LBC) layers insteadof normal convolution layers.
I Figure 1 shows the architecture of an LBC layer. LBC is acombination of 3 × 3 weight anchored convolution layer(-1,1,0), ReLU activation, and 1 × 1 learned convolution layer.
I The number of parameters:
p×h×w×q
Param. of CNN=Pram. of LBCNN
m×q
(1)
p×h×w
=m
∗(p,input channel; q,output channel; h and w,kernel size)
I Under the assumption of m = p, LBC can save up to h × w
parameters compared to normal convolution.

Problems
I Juefei-Xu et al. [1]discussed that for every output of normal
convolution layer d, there exists a vector v that makes output
0of LBC layer d equals to d:0(H ×W )×1
d = v ∗ σsigmoid (B ∗ X ) ∈ R(2)(H ×W )×1
= σrelu(w ∗ X ) ∈ R=d∗(B,Local Binary Filters; X,input; v,1×1 filters)
I Only when m > rank(X (H ×W )×m) the corresponding v mayexist.
I This m may always be very large, there may exist problems
when directly applying LBCNN.

Proposed Method
I We proposed a half normal half LBC architecture as a trade off
between accuracy and number of parameters.

I We design different series of Models based on DenseNet called
full-m and fused-m models (where m indicates the number ofLBC filters).
I Adjust m to change amount of learned parameters:

Figure 3: Relationship of Accuracy and Number of parameter (left), Relation
ship of Accuracy and Performance (right) .

I According to these results, increasing m helps, while also
increasing the number of parameters.
I Additional LBC layers makes model slower.

Conclusion
I We extend the method of LBCNN to larger model on ImageNet
to see if it helps.
I We analyzed the disadvantage of LBCNN and proposed a half
normal half LBC architecture as a trade off solution.
I LBCNN can’t get better results in more complex models andthis method seems effect the training speed a lot.
I To conclude, it is possible to get similar results with 3 × 3
convolution versus LBCNN method, while degradingperformance.

Future work
I Test LBCNN on more datasets and more models.
I Test LBCNN on more tasks besides image classification such as
semetic segmentation.
I Find some better approximate of normal convolution besides
LBCNN.

References
Felix Juefei-Xu, Vishnu Naresh Boddeti, and Marios Savvides.
Local Binary Convolutional Neural Networks.
In IEEE Computer Vision and Pattern Recognition (CVPR), July
2017.

Figure 2: Basic block of DenseNet full (left) and fused (right).

I Take Densely Connected Convolution Networks(DenseNet)[2]
as an example, as is shown in Figure 2.

Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q
Weinberger.
Densely connected convolutional networks.
In 2017 IEEE Conference on Computer Vision and PatternRecognition (CVPR), pages 2261–2269. IEEE, 2017.

121213Haoyu Zhang ,Mohamed Wahib ,Peng Chen , Satoshi Matsuokazhang.h.am@m.titech.ac.jp,mohamed.attia@aist.go.jp,chen.p.aa@m.titech.ac.jp,matsu@is.titech.ac.jp
1Tokyo Institute of Technology, Dept. of Mathematical and Computing Science, Tokyo, Japan
2AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory
3RIKEN Center for Computational Science,Hyogo,Japan