Fuzzy Set Theory in Computer Vision: Example 3, Part II
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1 Fuzzy Set Theory in Computer Vision: Example 3, Part II Derek T. Anderson and James M. Keller FUZZ-IEEE, July 2017
2 Overview Resource; CS231n: Convolutional Neural Networks for Visual Recognition CS231/tree/master/lectures Resource; CNN tutorial
3 DL, you need lots of data... Well, maybe...
4 DL, you need lots of data... Well, maybe... Most folks do NOT train from scratch
5 DL, you need lots of data... Well, maybe... Most folks do NOT train from scratch For example, maybe you train on ImageNet, which has a lot of data, and then you fine tune your network relative to your specific data/problem
6 DL, you need lots of data... Well, maybe... Most folks do NOT train from scratch For example, maybe you train on ImageNet, which has a lot of data, and then you fine tune your network relative to your specific data/problem Transfer learning
7 DL, you need lots of data... Well, maybe... Most folks do NOT train from scratch For example, maybe you train on ImageNet, which has a lot of data, and then you fine tune your network relative to your specific data/problem Transfer learning Hot field, recommend you do your homework
8 DL, you need lots of data... Well, maybe... Most folks do NOT train from scratch For example, maybe you train on ImageNet, which has a lot of data, and then you fine tune your network relative to your specific data/problem Transfer learning Hot field, recommend you do your homework For example, on small data sets maybe you just retrain the classifier? However, for medium size data sets maybe you retrain a bigger portion of the network or maybe all of it.
9 CNN basics Neurons typically arranged in width, height and depth
10 CNN basics Neurons typically arranged in width, height and depth For example Input: RGB, maybe 256x256x3
11 CNN basics Neurons typically arranged in width, height and depth For example Input: RGB, maybe 256x256x3 First conv: maybe 32 filters of size 5x5x3
12 CNN basics Neurons typically arranged in width, height and depth For example Input: RGB, maybe 256x256x3 First conv: maybe 32 filters of size 5x5x3 Output: filter maps, size 251x251x32
13 CNN basics Neurons typically arranged in width, height and depth For example Input: RGB, maybe 256x256x3 First conv: maybe 32 filters of size 5x5x3 Output: filter maps, size 251x251x32 Many different types of layers
14 Trivial example architecture Input, [32x32x3]
15 Trivial example architecture Input, [32x32x3] CONV layer, [32x32x12] (12 filters)
16 Trivial example architecture Input, [32x32x3] CONV layer, [32x32x12] (12 filters) RELU layer, maybe max(0,x) (stays [32x32x12])
17 Trivial example architecture Input, [32x32x3] CONV layer, [32x32x12] (12 filters) RELU layer, maybe max(0,x) (stays [32x32x12]) Max POOL layer, downsample, maybe get [16x16x12]
18 Trivial example architecture Input, [32x32x3] CONV layer, [32x32x12] (12 filters) RELU layer, maybe max(0,x) (stays [32x32x12]) Max POOL layer, downsample, maybe get [16x16x12] FC layer, MLP, output 5 neurons for 5 classes
19 Convolution Weight sharing (vs fully connected)
20 Convolution Weight sharing (vs fully connected) For example, if 32 filters and if each filter is say 7x5 size, then each filter results in a filter map of size (N (7 1))x(M (5 1)) if the input was size NxM and if no padding is used (varies per implementation)
21 Convolution Weight sharing (vs fully connected) For example, if 32 filters and if each filter is say 7x5 size, then each filter results in a filter map of size (N (7 1))x(M (5 1)) if the input was size NxM and if no padding is used (varies per implementation) Convolution slides the 7x5 filter (pattern signal) over every pixel (unless a stride, jump, is used) and it produces a value at each location telling us how similar the local spatial region is to that filter
22 Convolution Weight sharing (vs fully connected) For example, if 32 filters and if each filter is say 7x5 size, then each filter results in a filter map of size (N (7 1))x(M (5 1)) if the input was size NxM and if no padding is used (varies per implementation) Convolution slides the 7x5 filter (pattern signal) over every pixel (unless a stride, jump, is used) and it produces a value at each location telling us how similar the local spatial region is to that filter The deeper you go in the net, typically you move from micro to macro features, e.g., edges to maybe eyes to maybe faces, and many claim the deeper you go the more semantic information you start to mine as well...
23 Example of convolution at one spatial location
24 Example filters Example filters learned by Krizhevsky et al (AlexNet). Each of the 96 filters shown here is of size [11x11x3], and each one is shared by the neurons in one depth slice.
25 Full convolution
26 Pooling Progressively nonlinearlly reduces the spatial size of our signal
27 Pooling Progressively nonlinearlly reduces the spatial size of our signal Eliminate non-maximal values - reduce comp. in upper layers
28 Pooling Progressively nonlinearlly reduces the spatial size of our signal Eliminate non-maximal values - reduce comp. in upper layers Helps control overfitting as well to some extent
29 Pooling Progressively nonlinearlly reduces the spatial size of our signal Eliminate non-maximal values - reduce comp. in upper layers Helps control overfitting as well to some extent Translation and rotation and...
30 Pooling Death in the details
31 Pooling Death in the details The backward pass for a max(x,y) operation has a simple interpretation as only routing the gradient to the input that had the highest value in the forward pass. Hence, during the forward pass of a pooling layer it is common to keep track of the index of the max activation (sometimes also called the switches) so that gradient routing is efficient during backpropagation.
32 Pooling Death in the details The backward pass for a max(x,y) operation has a simple interpretation as only routing the gradient to the input that had the highest value in the forward pass. Hence, during the forward pass of a pooling layer it is common to keep track of the index of the max activation (sometimes also called the switches) so that gradient routing is efficient during backpropagation. Many use this with deconvolution (a different topic!) to decide how to reverse engineer the filters for purposes like visualization (see Zeiler et al., reverses via the switches, that induces a sparse map and various ways to impute the missing values)
33 Rectified linear unit (ReLU) Non-linear gating
34 Rectified linear unit (ReLU) Non-linear gating f (x) = max(0, x) Activation is simply thresholded at zero
35 Rectified linear unit (ReLU) Non-linear gating f (x) = max(0, x) Activation is simply thresholded at zero Does not saturate in positive region
36 Rectified linear unit (ReLU) Non-linear gating f (x) = max(0, x) Activation is simply thresholded at zero Does not saturate in positive region Was found to accelerate (factor of 6 inkrizhevsky et al.) convergence of stochastic gradient descent compared to sigmoid/tanh functions
37 Rectified linear unit (ReLU) Non-linear gating f (x) = max(0, x) Activation is simply thresholded at zero Does not saturate in positive region Was found to accelerate (factor of 6 inkrizhevsky et al.) convergence of stochastic gradient descent compared to sigmoid/tanh functions Simple to implement (versus alternative solutions, e.g., tanh/sigmoid)
38 Rectified linear unit (ReLU) Non-linear gating f (x) = max(0, x) Activation is simply thresholded at zero Does not saturate in positive region Was found to accelerate (factor of 6 inkrizhevsky et al.) convergence of stochastic gradient descent compared to sigmoid/tanh functions Simple to implement (versus alternative solutions, e.g., tanh/sigmoid) Leaky ReLU ( dying ReLU problem ) f (x) = 1(x < 0)(ax) + 1(x >= 0)(x) [a is a small constant]
39 Rectified linear unit (ReLU) Non-linear gating f (x) = max(0, x) Activation is simply thresholded at zero Does not saturate in positive region Was found to accelerate (factor of 6 inkrizhevsky et al.) convergence of stochastic gradient descent compared to sigmoid/tanh functions Simple to implement (versus alternative solutions, e.g., tanh/sigmoid) Leaky ReLU ( dying ReLU problem ) f (x) = 1(x < 0)(ax) + 1(x >= 0)(x) [a is a small constant] Other ones in the lit
40 Your all good, right? Nope... but a start
41 Your all good, right? Nope... but a start Stochastic gradient descent
42 Your all good, right? Nope... but a start Stochastic gradient descent Batch normalization
43 Your all good, right? Nope... but a start Stochastic gradient descent Batch normalization Architectures
44 Your all good, right? Nope... but a start Stochastic gradient descent Batch normalization Architectures Deconvolution
45 Your all good, right? Nope... but a start Stochastic gradient descent Batch normalization Architectures Deconvolution GPU acceleration
46 Your all good, right? Nope... but a start Stochastic gradient descent Batch normalization Architectures Deconvolution GPU acceleration Regularization and drop out...
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Fuzzy Set Theory in Computer Vision: Example 3 Derek T. Anderson and James M. Keller FUZZ-IEEE, July 2017 Overview Purpose of these slides are to make you aware of a few of the different CNN architectures
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