中国云 移动互联网创新大赛
-- 火眼金睛
团队：LCLL
Zhejiang University
Team
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Team Leader:
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Yue Lin (林悦): responsible for model training, parameters tuning, structure design
Team Members:
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Debing Zhang (张德兵): responsible for seeking the new techniques in DL, structure design
Cheng Li (李成): responsible for the images crawler, build the model training environment
Xiaoting Zhao (赵晓婷): responsible for the data labeling
Publications:
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Yue Lin, Rong Jin, Deng Cai, Xiaofei He: Random Projection with Filtering for Nearly Duplicate Search. AAAI 2012
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Yue Lin, Rong Jin, Deng Cai, Shuicheng Yan, Xuelong Li: Compressed Hashing. CVPR 2013
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Bin Xu, Jiajun Bu, Yue Lin, Chun Chen, Xiaofei He, Deng Cai: Harmonious Hashing. IJCAI 2013
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Zhongming Jin,Yao Hu,Yue Lin, Debing Zhang, Shiding Lin,Deng Cai, Xuelong Li: Complementary Projection Hashing. ICCV 2013
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Yao Hu, Debing Zhang, Jieping Ye, Xuelong Li, Xiaofei He: Fast and Accurate Matrix Completion via Truncated Nuclear Norm
Regularization. TPAMI 2013
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Yao Hu, Debing Zhang, Zhongming Jin, Deng Cai, Xiaofei He: Active Learning Based on Local Representation. IJCAI 2013
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Debing Zhang, Genmao Yang, Yao Hu, Zhongming Jin, Deng Cai, Xiaofei He: A Unified Approximate Nearest Neighbor Search Scheme by
Combining Data Structure and Hashing. IJCAI 2013
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Debing Zhang, Yao Hu, Jieping Ye, Xuelong Li, Xiaofei He: Matrix completion by Truncated Nuclear Norm Regularization. CVPR 2012
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Yao Hu, Debing Zhang, Jun Liu, Jieping Ye, Xiaofei He: Accelerated singular value thresholding for matrix completion. KDD 2012
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Zhongming Jin, Cheng Li, Deng Cai, Yue Lin: Densitive Sensitive Hashing. TSMCB 2013
数据
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传统做法：
Ref: Pedestrian Detection: An Evaluation of the State of the Art
比赛数据
We choose Deep Learning
Offline test result: 0.9820.
Structure
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We follow the structure used in MNIST, ImageNet
More data is good
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Negative Data: Caltech 256, Some images selected in VOC.
Caltech 256
All the data need to be
checked. Mislabeled images
will hurt the performance.
VOC
More data is good
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Positive Data: Baidu Shitu
 We implement a crawler to send some classical images to the
Baidu Shitu and save the results.
 save the page -> get the images’ link + another crawler
Training Information
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Gray vs. Color:
Color is better.
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Resolution:
Maps:
Convolutions:
128x128 is better than 64x64 and 32x32.
More maps is better but cost more
time. Finally we choose 64 maps.
More Convolutions is better but
cost more time. Finally we choose
5 convolutions.
Training Information
Local response normalization
We use ReLU neuron types f(x)=max(0,x). (Much faster)
“Local response normalization aids generalization.”
Parameters
Layers Type
Maps & neurons
0
input
3 maps of 128x128 neurons
1
convolutional
64 maps of 120x120 neurons
2
max-pool
64 maps of 40x40 neurons
3
normalization
64 maps of 40x40 neurons
4
convolutional
64 maps of 40x40 neurons
5
normalization
64 maps of 40x40 neurons
6
max-pool
64 maps of 20x20 neurons
7
convolutional
64 maps of 20x20 neurons
8
normalization
64 maps of 20x20 neurons
9
max-pool
64 maps of 10x10 neurons
10
convolutional
64 maps of 10x10 neurons
11
convolutional
64 maps of 10x10 neurons
12
Fully connected
512 neurons
13
Fully connected
2 neurons
Viewing the Net
Training and test error over time.
Viewing the Net
Viewing the Net
Viewing the Net
Viewing the Net
Discussion
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Dropout
Ref: ImageNet Classification with Deep Convolutional Neural Networks
 Achieve better performance on ImageNet, MNIST, TIMIT, CIFAR-10.
 In offline test, the performance is improved from 0.9820 to 0.9826.
Future
1.Why it works?
Theory Extension: Long way to go
2.How it goes?
Distributed Computation，Huge Data
Thank you