In the past few years, artificial neural networks evolved to reach a human-like performance in tasks such object recognition, speech and audio analysis, natural language processing and much more. This short master's degree presentation discuss some approaches. The complementary paper can be found in http://hiveorama.com/papers/DeepLearning-NelsonForte.pdf.

1. Deep learning
Modeling high-level face features
through deep networks

2. Explaining deep learning to my mommy

3. Biological motivation
[1]

4. Scale
● Low costs
○ ≈ $ 4000/m
(100ECUs e 10TB)
● Elastic clusters
● Network vs. Disk
● GPUs
● Data
o Variability
o Volume
o Easy acquisition

5. Learning
● Lower learning time
● Distributed networks
● Feature extraction
● Deepness with low
error rates
[2]

6. Convolutional Networks

7. Layer n - Primitive shapes
[3]

8. Layer n+1 - Complex shapes
[3]

9. Performance ILSVRC2013
Team Comments Error rate
Clarifai
Average of multiple models on original
training data.
0.11743
Clarifai
Another attempt at multiple models on
original training data.
0.1215
Clarifai Single model trained on original data. 0.12535
NUS
adaptive non-parametric rectification of
all outputs from CNNs and refined
PASCAL VOC12 winning solution, with
further retraining on the validation set.
0.12953
NUS
adaptive non-parametric rectification of
all outputs from CNNs and refined
PASCAL VOC12 winning solution.
0.13303
[4]

10. Image Classification

11. Clarifai results

12. Face features recognition

13. Clarifai results

14. References
1. Wong, Rachel et al. (2005): "Circuits of vertebrate retina". Nature
Magazine, Volume 9 Number 1.
2. Huang G.B., Lee H., Miller E. L. (2012): “Learning Hierarchical
Representations for Face Verification with Convolutional Deep Belief
Networks”
3. Ng A. et al (2012): “Emergence of Object-Selective Features in
Unsupervised Feature Learning.”
4. Image-net.org, (2014). ImageNet. Available at: http://www.image-net.org/.