From Zero to CNNs to Brains - CBMM...Vision Barbu,Mayo,Conwell (MIT,Harvard) 0 !CNNs Brains...
Transcript of From Zero to CNNs to Brains - CBMM...Vision Barbu,Mayo,Conwell (MIT,Harvard) 0 !CNNs Brains...
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From Zero to CNNs to Brains
Andrei Barbu, David Mayo, Colin Conwell
objectnet.dev0xab.com/papers/conwell-brains-mouse-cnn-2020.pdf
bit.ly/31IjH5ubit.ly/3fSAhVh
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
objectnet.dev0xab.com/papers/conwell-brains-mouse-cnn-2020.pdfbit.ly/31IjH5ubit.ly/3fSAhVh
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Vision
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Vision
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Vision
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Computer vision failures
NHTSA: Neither Autopilot nor the driver noticed the white side of thetractor-trailer against a brightly lit sky, so the brake was not applied.
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Computer vision failures
NHTSA: Neither Autopilot nor the driver noticed the white side of thetractor-trailer against a brightly lit sky, so the brake was not applied.
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Computer vision failures
NHTSA: Neither Autopilot nor the driver noticed the white side of thetractor-trailer against a brightly lit sky, so the brake was not applied.
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Baby machine learning
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Classifiers
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Classifiers
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Classifiers
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Linear classifiers
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Linear classifiers
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Linear classifiers
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Linear classifiers
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Linear classifiers
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Linear classifiers
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Linear classifiers
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Non-linearities: Sigmoid
−4 −2 2 4
−1
−0.5
0.5
11
1+e−x
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Gradient descent
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Gradient descent
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Linear classifiers and kernels
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Linear classifiers and kernels
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Linear classifiers and kernels
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Intro to PyTorch
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Convolution
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Image convolution
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Gaussian blur kernel
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Gaussian blur kernel
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Gaussian blur
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Early edge detection: Sobel
−1 0 +1−2 0 +2−1 0 +1
−1 −2 −10 0 0+1 +2 +1
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Early edge detection: Sobel
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Object detection: HoG
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Object detection: DPM
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Object detection: Deep learning
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Object detection
image as inputpropose fixed-size regions that are likely to have objectsfeed each through a network with many layerslinear classifier at the top
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Object detection
image as input
propose fixed-size regions that are likely to have objectsfeed each through a network with many layerslinear classifier at the top
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Object detection
image as inputpropose fixed-size regions that are likely to have objects
feed each through a network with many layerslinear classifier at the top
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Object detection
image as inputpropose fixed-size regions that are likely to have objectsfeed each through a network with many layers
linear classifier at the top
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Object detection
image as inputpropose fixed-size regions that are likely to have objectsfeed each through a network with many layerslinear classifier at the top
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Performance
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Vision . . .
A man and a child are sitting on a plane.
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Vision . . .
A man and a child are sitting on a plane.
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020
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Vision . . .
A man and a child are sitting on a plane.
Barbu, Mayo, Conwell (MIT, Harvard) 0→CNNs→Brains August 12, 2020