πΌ π - Politecnico di Milanohome.deib.polimi.it/boracchi/teaching/IC/IC_Lez1_a...Β Β· 2018-02-14Β Β· Convolutional Neural Networks ... Convolutional Neural Networks for Visual
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DateDeep Learnig Classes
(09:30-13:00)Image Classification Classes
(14:15-17:45)
12/02/2018Introduction to Deep Learning, Classification
and Feed Forward Neural NetworksIntroduction to Image Classification and basics
of image handling in Python
14/02/2018Overfitting and regularization, gradient descent
variations, tips & tricksHand-crafted features for image classification
16/02/2018Recurrent neural networks, vanishing gradient
issues, Long-Short Term Memories Computer Vision features for image
classification
19/02/2018 TensorFlow and PyTorchData-driven feature extraction and
Convolutional Neural Networks
21/02/2018Deep neural networks architectures for image
classification and structural learning (with guests)
Advanced CNNs and Best practicesin image classification
23/02/2018Special guests: Variational Autoencoder, ShapeClassification, Overview of DeepMind research.
An overview on extended problems in image classification
DateDeep Learnig Classes
(09:30-13:00)Image Classification Classes
(14:15-17:45)
12/02/2018Introduction to Deep Learning, Classification
and Feed Forward Neural NetworksIntroduction to Image Classification and basics
of image handling in Python
14/02/2018Overfitting and regularization, gradient
descent variations, tips & tricksHand-crafted features for image classification
16/02/2018Recurrent neural networks, vanishing gradient
issues, Long-Short Term Memories Computer Vision features for image
classification
19/02/2018 TensorFlow and PyTorchData-driven feature extraction and
Convolutional Neural Networks
21/02/2018Deep neural networks architectures for image
classification and structural learning (with guests)
Advanced CNNs and Best practicesin image classification
23/02/2018Special guests: Variational Autoencoder, ShapeClassification, Overview of DeepMind research.
An overview on extended problems in image classification
πΌ(π, π) = [120, πππ, 30]
πΌ(π, π) = [100, 190, πππ]
πΌ(π, π) = [πππ, 80, 70]
πΌ(π, π) = [40, 30, 11]
[0 β 255]
πΊ β βπ ΓπΆ
π΅ β βπ ΓπΆ
πΌ β βπ ΓπΆΓ3
π β βπ ΓπΆ
πΌ β β512Γ512Γ3
β’ π β βπ π
β’ π¦ β Ξ = {1, β¦ , πΏ} πΌ πΏ
ππ = { ππ , π¦π , π = 1, β¦ , π}π¦ βΆ βπ β Ξ
ΰ·π¦π = π¦ ππ
π¦π , βπ
ΰ·π¦π π¦
{ π₯π , π¦π , π = 1, β¦ }
ΰ·π¦π = π¦πβ , being πβ = argminπ=1β¦π
π( ππ , ππ)
π ππ , ππ = ππ β ππ π=
πππ π
β ππ π
π
πΎ β
ΰ·π¦π = π¦πβ , being πβ the mode of π°πΎ ππ
π°πΎ(ππ) πΎ ππ
πΎ
βπ
CS231n: Convolutional Neural Networks for Visual Recognition http://cs231n.github.io/
π¦π¦ π = ππ + π
π β βπΏΓπ π β βπΏ
π¦
π π
CS231n: Convolutional Neural Networks for Visual Recognition http://cs231n.github.io/
π¦
ΰ·π¦π = argmaxπ=1,..,πΏ
π π π
ππ ππ β
π¦ ππ = πππ + π
π¦
ππ ππ
π, π = argminπββπΏΓπ πβ βπΏ
ππ,π¦π βππ
β π, π¦π
π, π = argminπββπΏΓπ πβ βπΏ
ππ,π¦π βππ
β π, π¦π + π β(π, π)
π > 0
βπ
βπ
β2
π π₯1, π₯2 = π€1π₯1 + π€2π₯2 + π
π₯1, π₯2
π π₯1, π₯2 = 0
β2
βπ
Input image
Input image
Feat
ure
Ext
ract
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A
lgo
rith
ms
πΌ1 β βπ1Γπ1
πΌ1 β βπ2Γπ2
π± β βπ
π± β βπ
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ssif
ier
(π βͺ π Γ π)
βparcelβ
βdoubleβ
π¦ β Ξ
π¦ β Ξ
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