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Alma Mater Studiorum - University of Bologna
School of Science
Department of Computer Science and Engineering DISI
Deep Learning for Computer Vision
Candidate
dott. Vincenzo Lomonaco
Supervisor
prof. Davide Maltoni
Co-examiner
prof. Mauro Gaspari
A comparison between Convolutional Neural
Networks and Hierarchical Temporal Memories on
object recognition tasks
08.09.15 Vincenzo Lomonaco 2
ContentsBackground & Motivations
Objectives
Introduction
CNN and HTM
Key features
Implementations
NORB-sequences
Original NORB dataset
New benchmark design
Experiments and Results
Experiments design
Results
Conclusions
Contents
08.09.15 Vincenzo Lomonaco 3
ContentsBackground & Motivations
Objectives
Introduction
CNN and HTM
Key features
Implementations
NORB-sequences
Original NORB dataset
New benchmark design
Experiments and Results
Experiments design
Results
Conclusions
Contents
08.09.15 Vincenzo Lomonaco 4
Deep Learning
In the last decade, Deep Learning techniques have shown to
perform incredibly well on a large variety of problems both in
Computer Vision and Natural Language Processing, resulting in
the state of the art in many tasks.
08.09.15 Vincenzo Lomonaco 5
Deep Learning advantages
Deep Learning is a branch of machine learning based on a set of
algorithms that attempt to model high-level abstractions in data by
using model architectures composed of multiple non-linear
transformations.
08.09.15 Vincenzo Lomonaco 6
Deep Learning disadvantages
● Poorly understood surrounding theory
● Non-optimal method
● Very difficult to train
● Huge quantity of data needed
● High Performance Computing environment needed
Possible limitations:
08.09.15 Vincenzo Lomonaco 7
Objectives
Proving that taking inspiration from biological learning
systems can help again in advancing the field of DL.
Proving that, with less data, it is however possible to reach
good levels of accuracy.
08.09.15 Vincenzo Lomonaco 8
How
We would like to show that, with a lower quantity of available
data, HTM can outperfom CNN on these tasks remaining
comparable in terms of training times.
Comparing two very different deep learning algorithms on
object recognition tasks:
– CNN: classical approach, state-of-the-art for object
recognition
– HTM: new biologically inspired approach
08.09.15 Vincenzo Lomonaco 9
NORB-sequences
Experiments and Results
Conclusions
ContentsBackground & Motivations
Objectives
Introduction
CNN and HTM
Key features
Implementations
Original NORB dataset
New benchmark design
Experiments design
Results
Contents
08.09.15 Vincenzo Lomonaco 10
CNN
CNNs are MLP variants where individual neurons are tiled in
such a way that they respond to overlapping regions in the
visual field. They are architectural inspired by Hubel and
Wiesel’s early work on the cat’s visual cortex.
● Python
● Using Theano
● 11 source files, 2550+ lns
● Pure supervised method
● Sparse Connectivity
● Shared Weights
Key features: Implementation:
08.09.15 Vincenzo Lomonaco 11
HTM
HTM is known as a new emerging paradigm that is more
biologically inspired. It tries to incorporate concepts like time,
context and attention during the learning process that are
typical of the human brain.
● C#, OPENMP version
● Provided by Biometric
System Lab (DISI)
● Mainly unsupervised method
● Top down and bottom-up
information flow
● Bayesian probabilistic
formulation
Key features: Implementations:
08.09.15 Vincenzo Lomonaco 12
Experiments and Results
Conclusions
Original NORB dataset
New benchmark design
ContentsBackground & Motivations
Objectives
Introduction
CNN and HTM
Key features
Implementations
Experiments design
Results
Contents
NORB-sequences
08.09.15 Vincenzo Lomonaco 13
NORB-Sequences
Since the computer vision community is starting to investigate
object recognition algorithms on videos, we would like to move
our comparison to that direction.
To this purpose, a new benchmark of a large collection of image
sequences starting from the well-know small NORB DATASET
has been created.
THE original NORB DATASET:
● Stores 48,600 96x96 image (5 categories, 10 instances, 6 lightings,
9 elevations, and 18 azimuths).
● Is well-know and accepted by the research community in the
context of object-recognition
08.09.15 Vincenzo Lomonaco 14
original NORB DATASET
Training instances Test instances
08.09.15 Vincenzo Lomonaco 15
Java sequencer
NORB-sequences is made possible thanks to a Java software
that takes in input the small NORB DATASET, and given a
number of different tuning parameters, return a number of
training and a test image sequences.
time
● The sequences are created ad hoc to simulate a camera moving
around a specific object including changes in the surround lighting.
● Integrated KNN baseline, GUI, 10 source files, 2600+ lns
Key features:
08.09.15 Vincenzo Lomonaco 16
NORB Sequences GUI
08.09.15 Vincenzo Lomonaco 17
NORB-sequences
Conclusions
ContentsBackground & Motivations
Objectives
Introduction
CNN and HTM
Key features
Implementations
Original NORB dataset
New benchmark design
Experiments design
Results
Contents
Experiments and Results
08.09.15 Vincenzo Lomonaco 18
Experiments design
1) Validate the CNN implementation on the NORB dataset
2) Evaluate the performance of both algorithms on the plain
NORB dataset
3) Evaluate the performance of both algorithms on the NORB
sequences
08.09.15 Vincenzo Lomonaco 19
CNN validation
In order to validate the new implementation,the goal was to
reproduce Y. LeCun original results on the plain NORB
DATASET.
08.09.15 Vincenzo Lomonaco 20
Plain NORB results
Accuracy results comparison between CNN and HTM on the
plain NORB dataset.
08.09.15 Vincenzo Lomonaco 21
Training times
Training times comparison between CNN and HTM on the
NORB sequences.
Training size CNN times HTM times
100 + 800jit 10.94 m 21.19 m
250 + 2000jit 31.15 m 23.13 m
500 + 4000jit 38.24 m 22.14 m
1000 + 4000jit 91.26 m 26.04 m
2500 + 4000jit 94.90 m 61.08 m
5000 + 4000jit 124.7 m 89.58 m
10000 + 4000jit 187.7 m 143.5 m
24300 + 4000jit 51.31 m 596.2 m
● CNN: GPU Tesla C2075 Fermi
(GPU speedup x3.2)
● HTM: CPU Xeon W3550, 4
cores.
Architectures:
08.09.15 Vincenzo Lomonaco 22
NORB sequences results
Accuracy results comparison between CNN and HTM on the
NORB sequences.
Train Test dist. CNN HTM
2x20 1 84.1% 86.36%
2x20 2 82.96% 86.16%
2x20 3 82.26% 86.16%
2x20 4 81.35% 84.20%
3x20 1 89.27% 91.0%
3x20 2 88.46% 89.52%
3x20 3 88.42% 88.68%
3x20 4 88.0% 85.88%
5x20 1 95.11% 92.69%
5x20 2 94.4% 91.62%
5x20 3 93.1% 92.86%
5x20 4 92.13% 91.23%
Train Test dist. CNN HTM
2x20 1 37.56% 37.08%
2x20 2 34.38% 37.82%
2x20 3 30.71% 33.17%
2x20 4 25.47% 28.89%
3x20 1 49.89% 43.68%
3x20 2 48.01% 44.08%
3x20 3 40.56% 37.93%
3x20 4 33.77% 34.93%
5x20 1 55.17% 52.57%
5x20 2 52.55% 49.74%
5x20 3 45.86% 45.52%
5x20 4 40.08% 41.30%
5-classes 50-classes
08.09.15 Vincenzo Lomonaco 23
NORB-sequences
Experiments and Results
Conclusions
ContentsBackground & Motivations
Objectives
Introduction
CNN and HTM
Key features
Implementations
Original NORB dataset
New benchmark design
Experiments design
Results
Contents
08.09.15 Vincenzo Lomonaco 24
Conclusions
In this dissertation three different milestones have been
achieved:
1) A LeNet-7 with Theano has been successfully implemented.
2) A new benchmark for object recognition in image
sequences has been created.
3) HTM and CNN have been compared on different object
recognition tasks.
It has been proven that the HTM bio-inspired approach can
be highly competitive and could be instrumental for
advancing the field of Deep Learning
08.09.15 Vincenzo Lomonaco 25
The End
http://vincenzolomonaco.com
vincenzo.lomonaco@studio.unibo.it
“If we want machines to think, we need to teach them to see”
Fei-Fei Li, Stanford Computer Vision Lab
Thank you for your attention
Vincenzo Lomonaco

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Deep Learning for Computer Vision: A comparision between Convolutional Neural Networks and Hierarchical Temporal Memories on object recognition tasks - Slides

  • 1. Alma Mater Studiorum - University of Bologna School of Science Department of Computer Science and Engineering DISI Deep Learning for Computer Vision Candidate dott. Vincenzo Lomonaco Supervisor prof. Davide Maltoni Co-examiner prof. Mauro Gaspari A comparison between Convolutional Neural Networks and Hierarchical Temporal Memories on object recognition tasks
  • 2. 08.09.15 Vincenzo Lomonaco 2 ContentsBackground & Motivations Objectives Introduction CNN and HTM Key features Implementations NORB-sequences Original NORB dataset New benchmark design Experiments and Results Experiments design Results Conclusions Contents
  • 3. 08.09.15 Vincenzo Lomonaco 3 ContentsBackground & Motivations Objectives Introduction CNN and HTM Key features Implementations NORB-sequences Original NORB dataset New benchmark design Experiments and Results Experiments design Results Conclusions Contents
  • 4. 08.09.15 Vincenzo Lomonaco 4 Deep Learning In the last decade, Deep Learning techniques have shown to perform incredibly well on a large variety of problems both in Computer Vision and Natural Language Processing, resulting in the state of the art in many tasks.
  • 5. 08.09.15 Vincenzo Lomonaco 5 Deep Learning advantages Deep Learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures composed of multiple non-linear transformations.
  • 6. 08.09.15 Vincenzo Lomonaco 6 Deep Learning disadvantages ● Poorly understood surrounding theory ● Non-optimal method ● Very difficult to train ● Huge quantity of data needed ● High Performance Computing environment needed Possible limitations:
  • 7. 08.09.15 Vincenzo Lomonaco 7 Objectives Proving that taking inspiration from biological learning systems can help again in advancing the field of DL. Proving that, with less data, it is however possible to reach good levels of accuracy.
  • 8. 08.09.15 Vincenzo Lomonaco 8 How We would like to show that, with a lower quantity of available data, HTM can outperfom CNN on these tasks remaining comparable in terms of training times. Comparing two very different deep learning algorithms on object recognition tasks: – CNN: classical approach, state-of-the-art for object recognition – HTM: new biologically inspired approach
  • 9. 08.09.15 Vincenzo Lomonaco 9 NORB-sequences Experiments and Results Conclusions ContentsBackground & Motivations Objectives Introduction CNN and HTM Key features Implementations Original NORB dataset New benchmark design Experiments design Results Contents
  • 10. 08.09.15 Vincenzo Lomonaco 10 CNN CNNs are MLP variants where individual neurons are tiled in such a way that they respond to overlapping regions in the visual field. They are architectural inspired by Hubel and Wiesel’s early work on the cat’s visual cortex. ● Python ● Using Theano ● 11 source files, 2550+ lns ● Pure supervised method ● Sparse Connectivity ● Shared Weights Key features: Implementation:
  • 11. 08.09.15 Vincenzo Lomonaco 11 HTM HTM is known as a new emerging paradigm that is more biologically inspired. It tries to incorporate concepts like time, context and attention during the learning process that are typical of the human brain. ● C#, OPENMP version ● Provided by Biometric System Lab (DISI) ● Mainly unsupervised method ● Top down and bottom-up information flow ● Bayesian probabilistic formulation Key features: Implementations:
  • 12. 08.09.15 Vincenzo Lomonaco 12 Experiments and Results Conclusions Original NORB dataset New benchmark design ContentsBackground & Motivations Objectives Introduction CNN and HTM Key features Implementations Experiments design Results Contents NORB-sequences
  • 13. 08.09.15 Vincenzo Lomonaco 13 NORB-Sequences Since the computer vision community is starting to investigate object recognition algorithms on videos, we would like to move our comparison to that direction. To this purpose, a new benchmark of a large collection of image sequences starting from the well-know small NORB DATASET has been created. THE original NORB DATASET: ● Stores 48,600 96x96 image (5 categories, 10 instances, 6 lightings, 9 elevations, and 18 azimuths). ● Is well-know and accepted by the research community in the context of object-recognition
  • 14. 08.09.15 Vincenzo Lomonaco 14 original NORB DATASET Training instances Test instances
  • 15. 08.09.15 Vincenzo Lomonaco 15 Java sequencer NORB-sequences is made possible thanks to a Java software that takes in input the small NORB DATASET, and given a number of different tuning parameters, return a number of training and a test image sequences. time ● The sequences are created ad hoc to simulate a camera moving around a specific object including changes in the surround lighting. ● Integrated KNN baseline, GUI, 10 source files, 2600+ lns Key features:
  • 16. 08.09.15 Vincenzo Lomonaco 16 NORB Sequences GUI
  • 17. 08.09.15 Vincenzo Lomonaco 17 NORB-sequences Conclusions ContentsBackground & Motivations Objectives Introduction CNN and HTM Key features Implementations Original NORB dataset New benchmark design Experiments design Results Contents Experiments and Results
  • 18. 08.09.15 Vincenzo Lomonaco 18 Experiments design 1) Validate the CNN implementation on the NORB dataset 2) Evaluate the performance of both algorithms on the plain NORB dataset 3) Evaluate the performance of both algorithms on the NORB sequences
  • 19. 08.09.15 Vincenzo Lomonaco 19 CNN validation In order to validate the new implementation,the goal was to reproduce Y. LeCun original results on the plain NORB DATASET.
  • 20. 08.09.15 Vincenzo Lomonaco 20 Plain NORB results Accuracy results comparison between CNN and HTM on the plain NORB dataset.
  • 21. 08.09.15 Vincenzo Lomonaco 21 Training times Training times comparison between CNN and HTM on the NORB sequences. Training size CNN times HTM times 100 + 800jit 10.94 m 21.19 m 250 + 2000jit 31.15 m 23.13 m 500 + 4000jit 38.24 m 22.14 m 1000 + 4000jit 91.26 m 26.04 m 2500 + 4000jit 94.90 m 61.08 m 5000 + 4000jit 124.7 m 89.58 m 10000 + 4000jit 187.7 m 143.5 m 24300 + 4000jit 51.31 m 596.2 m ● CNN: GPU Tesla C2075 Fermi (GPU speedup x3.2) ● HTM: CPU Xeon W3550, 4 cores. Architectures:
  • 22. 08.09.15 Vincenzo Lomonaco 22 NORB sequences results Accuracy results comparison between CNN and HTM on the NORB sequences. Train Test dist. CNN HTM 2x20 1 84.1% 86.36% 2x20 2 82.96% 86.16% 2x20 3 82.26% 86.16% 2x20 4 81.35% 84.20% 3x20 1 89.27% 91.0% 3x20 2 88.46% 89.52% 3x20 3 88.42% 88.68% 3x20 4 88.0% 85.88% 5x20 1 95.11% 92.69% 5x20 2 94.4% 91.62% 5x20 3 93.1% 92.86% 5x20 4 92.13% 91.23% Train Test dist. CNN HTM 2x20 1 37.56% 37.08% 2x20 2 34.38% 37.82% 2x20 3 30.71% 33.17% 2x20 4 25.47% 28.89% 3x20 1 49.89% 43.68% 3x20 2 48.01% 44.08% 3x20 3 40.56% 37.93% 3x20 4 33.77% 34.93% 5x20 1 55.17% 52.57% 5x20 2 52.55% 49.74% 5x20 3 45.86% 45.52% 5x20 4 40.08% 41.30% 5-classes 50-classes
  • 23. 08.09.15 Vincenzo Lomonaco 23 NORB-sequences Experiments and Results Conclusions ContentsBackground & Motivations Objectives Introduction CNN and HTM Key features Implementations Original NORB dataset New benchmark design Experiments design Results Contents
  • 24. 08.09.15 Vincenzo Lomonaco 24 Conclusions In this dissertation three different milestones have been achieved: 1) A LeNet-7 with Theano has been successfully implemented. 2) A new benchmark for object recognition in image sequences has been created. 3) HTM and CNN have been compared on different object recognition tasks. It has been proven that the HTM bio-inspired approach can be highly competitive and could be instrumental for advancing the field of Deep Learning
  • 25. 08.09.15 Vincenzo Lomonaco 25 The End http://vincenzolomonaco.com vincenzo.lomonaco@studio.unibo.it “If we want machines to think, we need to teach them to see” Fei-Fei Li, Stanford Computer Vision Lab Thank you for your attention Vincenzo Lomonaco