Deep learning
Modeling high-level face features
through deep networks
Explaining deep learning to my mommy
Biological motivation
[1]
Scale
● Low costs
○ ≈ $ 4000/m
(100ECUs e 10TB)
● Elastic clusters
● Network vs. Disk
● GPUs
● Data
o Variability
o Volume...
Learning
● Lower learning time
● Distributed networks
● Feature extraction
● Deepness with low
error rates
[2]
Convolutional Networks
Layer n - Primitive shapes
[3]
Layer n+1 - Complex shapes
[3]
Performance ILSVRC2013
Team Comments Error rate
Clarifai
Average of multiple models on original
training data.
0.11743
Cla...
Image Classification
Clarifai results
Face features recognition
Clarifai results
References
1. Wong, Rachel et al. (2005): "Circuits of vertebrate retina". Nature
Magazine, Volume 9 Number 1.
2. Huang G....
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Deep learning: Modeling high-level face features through deep networks

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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.

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  • Invariance theory: Objects are recognized by structural information or individual parts. Rotation and completion are made by invariant brain models.
  • Especialização das células
    Células horizontais -> Reconhecer formas difusas
    Células bipolares -> Contraste e luz
    Células amácrinas -> Resolução (ajuste de foco)
    Células ganglionares -> Separação e caracteríticas
  • m1.medium
    HDD - 120MB/s
    SSD - 600MB/s
    GPUs +100x rápidos que CPUs (para operações matemáticas)

  • Representações automáticas = Aprendizado não-supervisionado de características
    Redes neurais menos direcionadas = Aprendem características gerais
  • Convolução resulta em aproveitamento de pesos.
  • Deep learning: Modeling high-level face features through deep networks

    1. 1. Deep learning Modeling high-level face features through deep networks
    2. 2. Explaining deep learning to my mommy
    3. 3. Biological motivation [1]
    4. 4. Scale ● Low costs ○ ≈ $ 4000/m (100ECUs e 10TB) ● Elastic clusters ● Network vs. Disk ● GPUs ● Data o Variability o Volume o Easy acquisition
    5. 5. Learning ● Lower learning time ● Distributed networks ● Feature extraction ● Deepness with low error rates [2]
    6. 6. Convolutional Networks
    7. 7. Layer n - Primitive shapes [3]
    8. 8. Layer n+1 - Complex shapes [3]
    9. 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. 10. Image Classification
    11. 11. Clarifai results
    12. 12. Face features recognition
    13. 13. Clarifai results
    14. 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/.

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