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verona_cnn.html
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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css">
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<script src="lib/colorStringStandalone.js" charset="utf-8"></script>
<script type="text/javascript" src="lib/jquery-2.2.4.min.js"></script>
<title>(Extremely) Quick DL Intro</title>
<meta name="description" content="Lectures at University of Verona">
<meta name="author" content="Sergey M Plis">
<meta name="apple-mobile-web-app-capable" content="yes">
<meta name="apple-mobile-web-app-status-bar-style" content="black-translucent">
<link rel="stylesheet" href="dist/reset.css">
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var link = document.createElement( 'link' );
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link.href = window.location.search.match( /print-pdf/gi ) ? 'css/print/pdf.css' : 'css/print/paper.scss';
document.getElementsByTagName( 'head' )[0].appendChild( link );
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<body>
<div class="reveal">
<!-- In between the <div="reveal"> and the <div class="slides">-->
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<!-- In between the <div="reveal"> and the <div class="slides">-->
<!-- Any section element inside of this container is displayed as a slide -->
<div class="slides">
<section>
<section>
<p>
<h1>Convolutional Neural Networks</h1>
<h2>Selected topics in Deep Learning</h2>
<h3>in 6 hours or less</h3>
<h3>by Sergey Plis</h3>
<p>
</section>
</section>
<!-- --------------------------------------------------------------------------->
<section>
<section>
<h2>History of CNNs</h2>
</section>
<section>
<h2>Cat's brain 1962 (Hubel and Wiesel)</h2>
<img width="70%"
src="figures/cats_brain.png" alt="cat's brain">
<div class="slide-footer">
<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359523/pdf/jphysiol01247-0121.pdf">Receptive fields, binocular interaction and functional architecture in the cat's visual cortex</a>
</div>
</section>
<section>
<div id="header-right" style="margin-right: -100px;">
<img width="130"
src="figures/fukushima.jpg" alt="fukushima">
</div>
<h2>Fukushima's Neurocognitron 1979</h2>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="900"
src="figures/neurocognitron_.png" alt="neurocognitron">
<div class="slide-footer">
<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1359523/pdf/jphysiol01247-0121.pdf">Neocognitron - A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position</a>
</div>
</section>
<section>
<div id="header-right" style="margin-right: -100px;">
<img width="130"
src="figures/waibel.jpg" alt="waibel">
</div>
<h2>Time Delay Neural Network 1989</h2>
<img style="margin-top: -20px;" width="40%"
src="figures/tdnn_.png" alt="tdnn">
<div class="slide-footer">
<a href="https://www.cs.toronto.edu/~fritz/absps/waibelTDNN.pdf">Phoneme Recognition Using Time Delay Neural Networks</a>
</div>
<aside class="notes">
Note 1D channels!
</aside>
</section>
<section>
<div id="header-right">
<img width="130"
src="figures/lecun.png" alt="lecun">
</div>
<h2>CNN 1989</h2>
<img style="margin-top: -50px;" width="73%"
src="figures/cnn.png" alt="cnn">
<div class="slide-footer">
<a href="http://yann.lecun.org/exdb/publis/pdf/lecun-89e.pdf">Backpropagation Applied to Handwritten Zip Code Recognition</a>
</div>
<aside class="notes">
Note undersampling to reduce layers!
</aside>
</section>
<section>
<div id="header-right">
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1); " width="130"
src="figures/lecun.png" alt="lecun">
</div>
<h2>CNN 1998</h2>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1000"
src="figures/lenet.png" alt="lenet">
<div class="slide-footer">
<a href="http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf">Gradient-Based Learning applied to Document Recognition</a>
</div>
<aside class="notes">
Note subsampling! No pooling yet!
</aside>
</section>
<section>
<div id="header-right">
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1); " width="130"
src="figures/dancirican.jpg" alt="cirican">
</div>
<h2>CNN+GPU+MaxPooling 2011</h2>
<img style="margin-top: -40px;" width="70%"
src="figures/gpunet.png" alt="gpunet">
<div class="slide-footer">
<a href="https://arxiv.org/pdf/1102.0183.pdf">High Performance Neural Network for Visual Object Classification</a>
</div>
<aside class="notes">
Everything is here. Max Pooling, GPU (60x speedup compared to CPU), lots of data etc.<br>
Chinese handwriting recognition 2011 winner.<br>
Traffic sign recognition winner 2011 <br>
Neuronal Membrane segmentation 2012 <br>
</aside>
</section>
<section>
<div id="header-right">
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1); " width="130"
src="figures/alex.png" alt="Alex">
</div>
<h2>AlexNet 2012</h2>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1000"
src="figures/alexnet.png" alt="alexnet">
<div class="slide-footer">
<a href="https://www.cs.toronto.edu/~fritz/absps/imagenet.pdf">ImageNet Classification with Deep ConvolutionalNeural Networks</a>
</div>
<aside class="notes">
</aside>
</section>
</section>
<!-- --------------------------------------------------------------------------->
<section>
<section>
<h2>CNN: bulding blocks</h2>
<div class="slide-footer">
<a href="http://deeplearning.net/software/theano_versions/dev/tutorial/conv_arithmetic.html">some images are from Theano documentation</a>
</div>
</section>
<section>
<h2>Convolving a kernel with an image</h2>
<row>
<col50>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1000"
src="figures/CNN_convo1.gif" alt="convolution">
\[
\left(
\begin{array}{ccc}
0 & 1 & 2 \\
2 & 2 & 0 \\
0 & 1 & 2 \\
\end{array}
\right)
\]
</col50>
<col50>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1000"
src="figures/CNN_convo2.gif" alt="convolution 2">
</col50>
</row>
</section>
<section>
<h2>Convolving a kernel with an image</h2>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1000"
src="figures/CNN_flat_conv.svg" alt="convolution">
<div class="slide-footer">
<a href="https://arxiv.org/pdf/1603.07285.pdf">A guide to convolution arithmetic for deep learning</a>
</div>
</section>
<section>
<h2>Padding and symmetries</h2>
<row>
<col50>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1000"
src="figures/CNN_same_pad.gif" alt="same pad">
</col50>
<col50>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1000"
src="figures/CNN_full_pad.gif" alt="full pad">
</col50>
</row>
</section>
<section>
<h2>Padding and symmetries</h2>
<img style="margin-top: -35px;" width="85%"
src="figures/CNN_padded.svg" alt="convolution">
<div class="slide-footer">
<a href="https://arxiv.org/pdf/1603.07285.pdf">A guide to convolution arithmetic for deep learning</a>
</div>
</section>
<section>
<h2>How do the channels look?</h2>
<div style="position:relative; width:800px; height:800px; margin:0 auto;">
<img class="fragment current-visible" data-transition="slide fade-out" data-fragment-index="0" width="600" src="figures/CNN_original.png" style="position:absolute;top:0;left:0;border:0; box-shadow: 0px 0px 0px rgba(255, 255, 255, 255);" />
<img class="fragment current-visible" data-transition="slide fade-out" data-fragment-index="1" width="600" src="figures/CNN_layer1.png" style="position:absolute;top:0;left:0;border:0; box-shadow: 0px 0px 0px rgba(255, 255, 255, 255);" />
<img class="fragment current-visible" data-transition="slide fade-out" data-fragment-index="2" width="600" src="figures/CNN_layer2.png" style="position:absolute;top:0;left:0;border:0; box-shadow: 0px 0px 0px rgba(255, 255, 255, 255);" />
<img class="fragment current-visible" data-transition="slide fade-out" data-fragment-index="3" width="600" src="figures/CNN_layer3.png" style="position:absolute;top:0;left:0;border:0; box-shadow: 0px 0px 0px rgba(255, 255, 255, 255);" />
</div>
</section>
<section>
<h2>Pooling: maxpooling</h2>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1000"
src="figures/CNN_maxpooling.gif" alt="max pooling">
</section>
<section>
<h2>Pooling: maxpooling</h2>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1000"
src="figures/CNN_flat_maxpooling.svg" alt="max pooling">
</section>
<section>
<h2>Pooling: average</h2>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1000"
src="figures/CNN_avpooling.svg" alt="average pooling">
</section>
<section>
<h2>How do we produce a class prediction?</h2>
</section>
<section>
<h2>One-convolution</h2>
<row>
<col50>
</col50>
<col50>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1000"
src="figures/CNN_one_conv.gif" alt="one_conv">
</col50>
</row>
</section>
<section>
<div id="header-right">
<img width="300"
src="figures/CNN_upconvolution.gif" alt="gif upconv">
</div>
<h2>Upconvolution</h2>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1000"
src="figures/CNN_upconvolution.png" alt="upconv">
<div class="slide-footer">
<a href="https://datascience.stackexchange.com/a/20176">Image from a comment on stackexchange</a>
</div>
</section>
<section>
<h2>Dilated convolution</h2>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="60%"
src="figures/CNN_dilation.gif" alt="dilated">
</section>
<section data-background-iframe="https://www.youtube.com/embed/HnWIHWFbuUQ?autoplay=1&controls=0&rel=0&modestbranding=1&showinfo=0">
<h2 style="text-shadow: 4px 4px 4px #002b36; color: #93a1a1">Play with a simulator</h2>
<h3 style="text-shadow: 4px 4px 4px #002b36; color: #93a1a1"><a href="https://www.youtube.com/watch?v=HnWIHWFbuUQ&featu" target="_blank">Video</a></h3>
<h3 style="text-shadow: 4px 4px 4px #002b36; color: #93a1a1"><a href="https://poloclub.github.io/cnn-explainer/" target="_blank">Demo</a></h3>
<h3 style="text-shadow: 4px 4px 4px #002b36; color: #93a1a1"><a href="https://github.com/poloclub/cnn-explainer" target="_blank">GitHub</a></h3>
<div class="slide-footer">
<a href="https://arxiv.org/abs/2004.15004v1">CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization</a>
</div>
</section>
<section>
<h2>Basic building blocks</h2>
<ol>
<li> Convolution with a filter
<li> Zero Padding
<li> Channels and channel-kernel relationship
<li> Pooling (max and average)
<li> Moving from convolution layers to predictions
<li> One convolution
<li> Upconvolution
<li> Dilated convolution
</ol>
</section>
</section>
<!-- --------------------------------------------------------------------------->
<section>
<section>
<h2>Skip connections</h2>
<aside class="notes">
Thanks to the reverse mode accumulation of Bert Speelpenning, (pause) after the forward pass backpropagation works backward along the paths in the computation graph. Normally, each edge denotes a computation, such as linear transformation followed by a slight nonlinearity. However, there is an advantage in excluding computation on some edges simply using an identity transform instead. A trick, that although, as everything under the sun, been known for a while, recently rediscovered to be effective in training very deep deep-learning models.<br>In this section I will walk you through the rediscovery and along the way introduce another transformative concept of gates.
</aside>
</section>
<section>
<h2>Dark knowledge</h2>
<row>
<col50>
</col50>
<col50>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1000"
src="figures/shadow-of-the-vampire.jpg" alt="vampire">
</col50>
</row>
<div class="slide-footer">
<a href="https://arxiv.org/abs/1312.6184">Do Deep Nets Really Need to be Deep?</a><br>
<a href="https://arxiv.org/abs/1503.02531">Distilling the Knowledge in a Neural Network</a>
<a href="https://arxiv.org/abs/1412.6550">FitNets: Hints for Thin Deep Nets</a>
</div>
<aside class="notes">
It has been conjectured quite early that deeper models should be more powerful than shallow, but we were unable to train them effectively no matter how hard we've tried. Various pre-training approaches, when parameters of each layer are initialized in a smart way, were helping only to a point. The difficulties were discouraging and we did not know how to move forward with thin and deep networks.<br>
Until in 2013 Rich Caruana observed the following effect: (Explain the mimic nets for teacher and student training) <br>
Geoff Hinton - called the grandfather of deep learning by some - came up with explanation, which he termed: dark knowledge (explain hard labels and soft labels) <br>
and the team of Yoshia Bengio has used this observation for training deep and thin networks they called fitnets<br>
yet, to some, this two stage teacher+student process seemed a bit cumbersome.
</aside>
</section>
<section>
<h3>Highway networks (May 2015 on arxiv)</h2>
<ul style="list-style-type: none; font-size: 32px;">
<li class="fragment roll-in">
$$
\vec{y} = H(\vec{x}, \bm{W}_H)
$$
<li class="fragment roll-in">
$$
\vec{y} = H(\vec{x}, \bm{W}_H) \odot T(\vec{x}, \bm{W}_T) + \vec{x} \odot C(\vec{x}, \bm{W}_C)
$$
<li class="fragment roll-in">
$$
\vec{y} = H(\vec{x}, \bm{W}_H) \odot T(\vec{x}, \bm{W}_T) + \vec{x} \odot (1 - T(\vec{x}, \bm{W}_T))
$$
<li class="fragment roll-in">
$$
\vec{y} =
\left\{
\begin{array}{ll}
\vec{x} & \mbox{if }\;\;T(\vec{x}, \bm{W}_T)=0,\\
H(\vec{x}, \bm{W}_H) & \mbox{if }\;\;T(\vec{x}, \bm{W}_T)=1
\end{array}
\right.
$$
<li class="fragment roll-in"> What if untrained gate is always open and does not let gradients flow?
<li class="fragment roll-in"> <b>Initialize gate biases to large negative values!</b>
</ul>
<div class="slide-footer" style="text-align: left;">
<a href="https://arxiv.org/abs/1505.00387">Highway Networks</a><br>
<a href="https://arxiv.org/abs/1507.06228">Training Very Deep Networks</a>
</div>
<aside class="notes">
In 2015 Rupesh Srivastawa, Klaus Greff and Yurgen Schmidhuber made the following, a bit elaborate, observation. If we embellish the regular layer in a feed forward neural network with a transfer and carry gates, we will enable gradients flow along the entire depth of a deep network.<br>
Let's see how they have done it.
</aside>
</section>
<section data-fullscreen>
<alert>Train models with 100 of layers instead of just 10 before</alert>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1500"
src="figures/CNN_HW_performance.png" alt="HWN">
<aside class="notes">
The networks that they have trained using traditional layers and highway layers were of depth 10, 20, 50, and a 100. Plain networks would train increasingly poorly with depth, while highway networks retained ability to train. Importantly the results were comparable with the ones of fitnets (trained by mimicing a teacher).
</aside>
</section>
<section>
<h2>Residual Networks (block)</h2>
<row>
<col50>
</col50>
<col50>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1500"
src="figures/CNN_resnets_block.svg" alt="resnet block">
</col50>
</row>
<div class="slide-footer">
<a href="https://arxiv.org/pdf/1512.03385.pdf">Deep Residual Learning for Image Recognition</a>
</div>
</section>
<section>
<h2>Residual Networks (full)</h2>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1500"
src="figures/CNN_resnets_whole.svg" alt="resnet whole">
<div class="slide-footer">
<a href="https://arxiv.org/pdf/1512.03385.pdf">Deep Residual Learning for Image Recognition</a>
</div>
</section>
<section>
<h2>Residual Networks (performance)</h2>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1500"
src="figures/CNN_resnets_perf.svg" alt="resnet perf">
<div class="slide-footer">
<a href="https://arxiv.org/pdf/1512.03385.pdf">Deep Residual Learning for Image Recognition</a>
</div>
</section>
<section>
<h2>Error surface effect of skip connection</h2>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1000"
src="figures/skip_smooth.png" alt="landscape">
<div class="slide-footer">
<a href="https://arxiv.org/pdf/1512.03385.pdf">Deep Residual Learning for Image Recognition</a><br>
<a href="https://arxiv.org/pdf/1505.00387.pdf">Highway Networks</a><br>
<a href="https://papers.nips.cc/paper/7875-visualizing-the-loss-landscape-of-neural-nets.pdf">Visualizing the Loss Landscape of Neural Nets</a>
</div>
<aside class="notes">
</aside>
</section>
<section>
<h2>Dense Networks (architecture)</h2>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="80%"
src="figures/CNN_densenet.svg" alt="dense diagram">
<div class="slide-footer">
<a href="https://arxiv.org/abs/1608.06993">Densely Connected Convolutional Networks</a>
</div>
<aside class="notes">
</aside>
</section>
<section>
<h2>Dense Networks (effect)</h2>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1000"
src="figures/densenet.png" alt="dense">
<div class="slide-footer">
<a href="https://arxiv.org/abs/1608.06993">Densely Connected Convolutional Networks</a>
</div>
<aside class="notes">
</aside>
</section>
<section>
<h2>Take Away Concepts</h2>
<ol>
<li> Skip connections
<li> Gates
</ol>
</section>
</section>
<!-- --------------------------------------------------------------------------->
<section>
<section>
<h2>Fully convolutional networks</h2>
</section>
<section data-background-iframe="https://www.youtube.com/embed/xr_2dwZDH6U?autoplay=1&controls=0&rel=0&modestbranding=1&showinfo=0">
<h2 style="color:#839496;">The task of Semantic segmentation</h2>
</section>
<section data-fullscreen>
<h2>Semantic segmentation task</h2>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1000"
src="figures/CNN_semantic_bikes.png" alt="fikes">
<div class="slide-footer">
<a href="https://arxiv.org/abs/1411.4038">Fully Convolutional Networks for Semantic Segmentation</a><br>
</div>
</section>
<section>
<h3>Replacing feed forward with convolutional</h3>
<ul style="list-style-type: none; margin: 0px 0;">
<li class="fragment roll-in">
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="800"
src="figures/CNN_semantic_cc_1.png" alt="cc 1">
<li class="fragment roll-in">
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="800"
src="figures/CNN_semantic_cc_2.png" alt="cc 1">
<li class="fragment roll-in">
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="800"
src="figures/CNN_semantic_cc_3.png" alt="cc 1">
</ul>
</section>
<section data-fullscreen>
<h2>Fully Convolutional Model (2014)</h2>
<img style="border:0; box-shadow: 0px 0px 0px rgba(150, 150, 255, 1);" width="1000"
src="figures/CNN_semantic_ff.png" alt="final model">
<div class="slide-footer">
<a href="https://arxiv.org/abs/1411.4038" target="_blank">Fully Convolutional Networks for Semantic Segmentation</a><br>
</div>
</section>
<section data-fullscreen>
<h2>Examples</h2>
<img width="50%"
src="figures/CNN_semantic_examples.png" alt="final model">
<div class="slide-footer">
<a href="https://arxiv.org/abs/1411.4038" target="_blank">Fully Convolutional Networks for Semantic Segmentation</a><br>
</div>
</section>
<section>
<h2>Take Away Point</h2>
<ol>
<li> When target and input have the same dimension it may be better to use convolution everywhere.
</ol>
</section>
</section>
<!-- --------------------------------------------------------------------------->
<section>
<section>
<h2>Semantic segmentation with twists</h2>
</section>
<section>
<h2>deep learning standard: U-net</h2>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="250"
src="figures_ccd/unet_arc.png" alt="david">
<p>
<div class='slide-footer'>
Ronneberger et al. U-net: Convolutional networks
for biomedical image segmentation. MICCAI 2015<br>
Çiçek et al. 3D U-Net: learning dense volumetric
segmentation from sparse annotation. MICCAI 2016
</div>
</section>
<section>
<h2>deep learning standard: U-net</h2>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="250"
src="figures_ccd/unet_table.png" alt="david">
<p>
<div class='slide-footer'>
Ronneberger et al. U-net: Convolutional networks for
biomedical image segmentation. MICCAI 2015<br>
Çiçek et al. 3D U-Net: learning dense volumetric
segmentation from sparse annotation. MICCAI 2016
</div>
</section>
<section>
<h2>comparison on the brain segmentation task</h2>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="250"
src="figures_ccd/tasks.png" alt="david">
</section>
<section>
<h2>state of the art: freesurfer</h2>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="250"
src="figures/CNN_freesurfer.png" alt="david">
<br>
<div class='slide-footer'>
Dale et al. Cortical surface-based analysis. I. Segmentation and
surface reconstruction. Neuroimage 1999
</div>
</section>
<section>
<h2>Meshnet</h2>
<img src="figures_ccd/dilation.png" style="border:0; box-shadow: 0px
0px 0px rgba(150, 150, 255, 0.8);float: left;"
alt="Websocket" width="40%">
<img src="figures_ccd/mn1.png" style="border:0; box-shadow: 0px
0px 0px rgba(150, 150, 255, 0.8);float: right;"
alt="Websocket" width="35%"
>
<div class="ulist">
<ul style="width: 60%; float: left;">
<li>Gray and White matter</li>
<li>FreeSurfer for ground truth</li>
<li>T1 MRIs from HCP</li>
<li>GitHub <br>
<small>
<a href="https://github.com/Entodi/MeshNet">
https://github.com/Entodi/MeshNet</a>
</small>
</li>
</ul>
</div>
<br>
<div class="slide-footer">
Fedorov et al. End-to-end learning of brain tissue segmentation
from imperfect labeling. IJCNN 2017
</div>
</section>
<section>
<h2>Meshnet</h2>
<img src="figures_ccd/mntable.png" style="border:0; box-shadow: 0px
0px 0px rgba(150, 150, 255, 0.8);float: right;"
alt="Websocket" width="60%"
>
<div class="ulist">
<ul style="width: 35%; float: left;">
<li>72516 vs. 23523355</li>
<li>600kb vs. 2Gb</li>
</ul>
</div>
<p>
<br>
<div class="slide-footer">
Fedorov et al. End-to-end learning of brain tissue segmentation
from imperfect labeling. IJCNN 2017
</div>
</section>
<section>
<h2>Meshnet</h2>
<img src="figures_ccd/mnpipeline.png" style="border:0; box-shadow: 0px
0px 0px rgba(150, 150, 255, 0.8);"
alt="Websocket" class="stretch"
>
<br>
<div class="slide-footer">
Fedorov et al. End-to-end learning of brain tissue segmentation
from imperfect labeling. IJCNN 2017
</div>
</section>
<section>
<h2>Meshnet</h2>
<img src="figures_ccd/mnexample.png" style="border:0; box-shadow: 0px
0px 0px rgba(150, 150, 255, 0.8);"
alt="Websocket" class="stretch"
>
<br>
<div class="slide-footer">
Fedorov et al. End-to-end learning of brain tissue segmentation
from imperfect labeling. IJCNN 2017
</div>
</section>
<section>
<h3>(often) better than the teacher</h3>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="700"
src="figures_ccd/MN_examples.png" alt="loop">
</section>
<section>
<h3>multimodal is straightforward</h3>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="700"
src="figures_ccd/MeshNet_multi.png" alt="loop">
</section>
<section>
<h3>better than the human (sometimes)</h3>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);" width="700"
src="figures_ccd/mnhuman.png" alt="loop">
</section>
<section>
<h3>better than U-net</h3>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);"
src="figures_ccd/mnvsunet.png" alt="loop">
</section>
<section data-background-video="figures_ccd/combined.mp4" data-background-size="contain" data-background-video-loop=true>
</section>
<section data-vertical-align-top data-background-iframe="https://brainchop.org"
data-background-interactive>
<h3 style="text-shadow: 4px 4px 4px #002b36; color: #93a1a1">Give us <i class="fas fa-star"></i> <i class="fas fa-star"></i> <i class="fas fa-star"></i> <i class="fas fa-star"></i> <i class="fas fa-star"></i> on <i class="fab fa-github"></i></h3>
<div class='slide-footer'>
<a href="https://github.com/neuroneural/brainchop">https://github.com/neuroneural/brainchop <i class="fab fa-github"></i></a>
</div>
</section>
</section>
<!-- --------------------------------------------------------------------------->
<section>
<section>
<h2>(even more) "Advanced" uses of CNN</h2>
</section>
<section>
<h2>Masked Convolutions</h2>
<div class="slide-footer">
<a href="https://arxiv.org/abs/1601.06759">
Pixel Recurrent Neural Networks
</a><br>
<a href="https://arxiv.org/abs/1606.05328">
Conditional Image Generation with PixelCNN Decoders
</a>
</div>
</section>
<section>
<h2>PixelCNNs</h2>
<div class="slide-footer">
<a href="https://arxiv.org/abs/1601.06759">
Pixel Recurrent Neural Networks
</a><br>
<a href="https://arxiv.org/abs/1606.05328">
Conditional Image Generation with PixelCNN Decoders
</a>
</div>
</section>
<section>
<h2>Wavenet: $\ge$16kHz audio</h2>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);"
src="figures/CNN_audio_zoom.gif" alt="loop">
<div class="slide-footer">
<a href="https://arxiv.org/abs/1609.03499">
WaveNet: A Generative Model for Raw Audio
</a>
</div>
</section>
<section>
<h2>Wavenet: sample by sample</h2>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);"
src="figures/CNN_wavenet.gif" alt="loop">
<div class="slide-footer">
<a href="https://arxiv.org/abs/1609.03499">
WaveNet: A Generative Model for Raw Audio
</a>
</div>
</section>
<section>
<h2>Wavenet: conditioned on text</h2>
<row>
<col50>
<table style="font-size:28px">
<tr>
<th>Model</th>
<th>"The blue lagoon..."</th>
</tr>
<tr>
<td>Parametric</td>
<td><audio controls="" src="https://storage.googleapis.com/deepmind-media/research/WaveNet/US%20English/parametric-1.wav"></audio></td>
</tr>
<tr>
<td>Concatenative</td>
<td>
<audio controls="" src="https://storage.googleapis.com/deepmind-media/research/WaveNet/US%20English/concatenative-1.wav"></audio>
</td>
</tr>
<tr>
<td>Wavenet</td>
<td>
<audio controls="" src="https://storage.googleapis.com/deepmind-media/research/WaveNet/US%20English/wavenet-1.wav"></audio>
</td>
</tr>
</table>
</col50>
<col50>
<table style="font-size:28px">
<tr>
<th>Model</th>
<th>"English poetry and ..."</th>
</tr>
<tr>
<td>Parametric</td>
<td><audio controls="" src="https://storage.googleapis.com/deepmind-media/research/WaveNet/US%20English/parametric-2.wav"></audio></td>
</tr>
<tr>
<td>Concatenative</td>
<td>
<audio controls="" src="https://storage.googleapis.com/deepmind-media/research/WaveNet/US%20English/concatenative-2.wav"></audio>
</td>
</tr>
<tr>
<td>Wavenet</td>
<td>
<audio controls="" src="https://storage.googleapis.com/deepmind-media/research/WaveNet/US%20English/wavenet-2.wav"></audio>
</td>
</tr>
</table>
</col50>
</row>
<audio controls="" src="https://storage.googleapis.com/deepmind-media/research/WaveNet/Speaker%201/speaker-2.wav"></audio>
<div class="slide-footer">
<a href="https://arxiv.org/abs/1609.03499">
WaveNet: A Generative Model for Raw Audio
</a>
</div>
</section>
<section data-fullscreen>
<h2>Deformable Convolutions</h2>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);"
src="figures/CNN_deformable.svg" alt="defrmable">
<div class="slide-footer">
<a href="https://arxiv.org/abs/1703.06211">
Deformable Convolutions
</a>
</div>
</section>
<section data-fullscreen>
<h2>Deformable Convolutions</h2>
<img class="stretch" style="border:0; box-shadow: 0px 0px 0px
rgba(150, 150, 255, 0.8);"
src="figures/CNN_deformable_conv_demo1.png" alt="defrmable">
<div class="slide-footer">
<a href="https://arxiv.org/abs/1703.06211">
Deformable Convolutions
</a>
</div>
</section>
<section>
<h2>Take Away Points</h2>
<ol>
<li> Masked convolution
<li> Pixel based generation
<li> Deformable convolution (can be rotation invariant)
</ol>
</section>
</section>
</div>
</div>
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