Francois Chollet //cs.brown.edu/courses/cs143/2017_Fall/lectures... · 2017. 11. 3. · Learning...
Transcript of Francois Chollet //cs.brown.edu/courses/cs143/2017_Fall/lectures... · 2017. 11. 3. · Learning...
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Francois Chollet - https://blog.keras.io/the-limitations-of-deep-learning.html
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3rd November 2017
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Yes, it’s abrain image: (
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Su et al., One pixel attack for fooling deep neural networkshttps://arxiv.org/abs/1710.08864
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https://arxiv.org/pdf/1707.07397.pdf
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Connectomics:Neural nets for neural nets
[PatricHagmann]
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Vision for understanding the brain
1mm cubed of brain
Image at 5-30 nanometers
How much data?
[Kaynig-Fittkau et al.]
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Vision for understanding the brain
1mm cubed of brain
Image at 5-30 nanometers
How much data?
1 Petabyte –1,000,000,000,000,000
~ All photos uploaded to Facebook per day
[Kaynig-Fittkau et al.]
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[Kaynig-Fittkau et al.]
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[Kaynig-Fittkau et al.]
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Vision for understanding the brain
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[Haehn et al.]
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[Haehn et al.]
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Network Architecture
[Haehn et al.]
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[Haehn et al.]
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Any directed acyclic graph (DAG) of differentiable modules is allowed.
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What about learning across ‘domains’?
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Two-stream networks –action recognition
[Simonyan et al. 2014]
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[Simonyan et al. 2014]
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Learning Deep Representations For Ground-to-Aerial Geolocalization
Tsung-Yi Lin, Yin Cui, Serge Belongie, James Hays
CVPR 2015
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Where was the photo taken?
View From Your Window ContestJune 9, 2010 – Feb. 4, 2015
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Ans:Milano, Italy
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To Geolocalize a Photo
• One can capture every corner on the earth
…
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To Geolocalize a Photo
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How To Match Ground-to-Aerial?
Shan et al., Accurate Geo-registration by Ground-to-Aerial Image Matching, 3DV’14Bansal et al., Ultra-wide baseline façade matching for geo-localization, ECCV workshop’12
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Are these the same location?
Ground
Aerial
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Are these the same location?
Ground
Aerial
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Cross-view Pairs
Ground
Aerial
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“Siamese” ConvNet for Ground-to-Aerial Matching
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“Siamese” ConvNet for Ground-to-Aerial Matching
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“Siamese” ConvNet for Ground-to-Aerial Matching
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For ground-aerial image pairs, should A, B networks share thesame weights?
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Quantitative Evaluation
Minor improvement with ‘domain-specific’ weights.
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Big space of designs!But we still don’t even know how many layers we need.
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VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
Karen Simonyan & Andrew Zisserman 2015
These are the pre-trained “VGG” networks
that you use in project 5
Beyond AlexNet
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Google LeNet (2014)
22 layers
6.67% errorImageNet top 5
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Inception!
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Parallel layers
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ResNet (He et al., 2015)
ResNet won ILSVRC 2015 with a top-5 error rate of 3.6%
Depending on their skill and expertise, humans generally hover around a 5-10% error.
But the task is arguablynot well defined.
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CIFAR-10
• 60,000 32x32 color images, 10 classes
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Regular net
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Residual Unit
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Residual Unit
The inputs of a lower layer is made available to a node in a higher layer.
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Why so steep?
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Flat regions in energy landscape
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Compute vs. parameters / multiply-adds
Hmm…efficient nets…might be useful for final project ???
James, do we have to go deeper?
https://www.infoq.com/news/2017/06/google-mobilenets-tensorflowhttps://arxiv.org/abs/1704.04861