My slides from Machine Learning Workshop Galicia 2016, organized by CESGA in Santiago de Compostela

1. Machine Learning Workhop Galicia 2016
Using Deep Neural Networks for Discriminative Feature Localization
Javier Sánchez Rois
Daniel González Jiménez Àgata Lapedriza García

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Àgata Lapedriza
Associate Professor
Javier Sánchez Rois
Research Software Engineer
Daniel González
R&D Director, MMI Area
Àgata Lapedriza
Associate Professor (UOC)
Visiting Researcher (MIT)

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Deep {Neural Nets, Learning}

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Deep Learning
43 y.
1.70 m
Iowa
woman
Deep Learning
26 y.
1.68 m
Louisiana
man

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Deep Learning

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Deep Learning

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A Linear Model

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(raw scores)

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(one-hot encoding)

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(... almost)
Logistic Regression

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?
Gradient Descent

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Linear Models: Pros
Easy to solve for minimal loss
GPUs for optimal implementation

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Linear Models: Cons
Underfitting complex data
OK for easy scenarios

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We need to go nonlinear

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?

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RELU: rectified linear unit
(scumbag nonlinear function)

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Fully-connected layer
RELU
The perceptron

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The perceptron

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The perceptron

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The multilayer perceptron

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Gradient Descent
too hard

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Backpropagation*
Backpropagation = Rule of Chain

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Training
1. Forward Propagation

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2. Backpropagation of error
Training

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Training
3. Parameter update

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4. Repeat …

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Andrej Karpathy - CS231n: Convolutional Neural Networks for Visual Recognition.

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Neural Nets are machine learning network characterized by
- Use of nonlinear models
- Backpropagation algorithm for training

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Deep Learning
DNN’ing images

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Image
kernel
Filtering result

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* =

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* =[ ] [ ]

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?
* =

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? ?

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? ?
** · ·
conv0 conv1
fc0 relu0 fc1

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How many convolutional layers?
How many fully connected layers?
Parameters?
…
Network Architecture

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CNN architectures: AlexNet (2012)

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CNN architectures: GoogLeNet (2014)

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Caffe
Model Zoo

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Discriminative Feature Localization

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Fine-tuning
Adjusting the parameters of an existing, pre-trained
network to solve a different problem with “few” images
from the new problem domain.

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Fine-tuning
** · ·
conv0 conv1
fc0 relu0 fc1
Pretrained network for problem P

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Fine-tuning
** · ·
conv0 conv1
fc0 relu0 fc1*
Substitute layer(s)

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Fine-tuning
** · ·
conv0 conv1
fc0 relu0 fc1*
Train with new samples for problem Q

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Fine-tuning
** · ·
conv0 conv1
fc0 relu0 fc1*
Train with new samples for problem Q

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Image Semantics

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Image Semantics

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Experiment: Localization using CAM
2 classes : {person, not person}

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Experiment: Sentiment Analysis + CAM
6 classes : {anger, disgust, fear, joy, sadness, surprise}

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New Challenges: Stable Object Detection

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THANK YOU!
Questions?