ECS 289G UC Davis Paper Presenta6on #1
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1 ECS 289G UC Davis Paper Presenta6on #1 ImageNet Classifica6on with Deep Convolu6onal Neural Networks Mohammad Motamedi Mohammad Motamedi ECS 289G PAPER PRESENTATION - UC DAVIS 1
2 Convolu6onal Neural Networks (CNNs) Easier to Train Much Fewer ConnecEon Using locality of pixel dependency Capacity is funceon of depth and breadth Image source: stackexchange.com Mohammad Motamedi ECS 289G PAPER PRESENTATION - UC DAVIS 2
3 Training Examples ImageNet Dataset of 15 million labeled high resolueon images categories Various image resolueons Data size 1.2 million training examples validaeon images teseng images Preprocessing Down- sampled to SubtracEng mean acevity over training set from each pixel Mohammad Motamedi Image source: image- net.org ECS 289G PAPER PRESENTATION - UC DAVIS 3
4 The Architecture Innova6ons and Details Mohammad Motamedi ECS 289G PAPER PRESENTATION - UC DAVIS 4
5 Rec6fied Linear Units (ReLU) Using f(x)=max (0, x) instead of tanh (x) No input normalizaeon is required for saturaeon preveneon Image source: cs231n.github.io Mohammad Motamedi ECS 289G PAPER PRESENTATION - UC DAVIS 5
6 Local Response Normaliza6on Normalizing over n adjacent feature maps at the same spaeal posieon. It is performed ayer applying ReLU. Effect Reduces top 1 error by 1.4 % Reduces top 5 error by 1.2 % Image source: computer.org Mohammad Motamedi ECS 289G PAPER PRESENTATION - UC DAVIS 6
7 Overlapping Pooling Pooling grid of space 2 are used for summarizing neighborhoods of size 3 3. Effects Reduces the top 1 error rate by 0.4 % Reduces the top 5 error rate by 0.4 % Mohammad Motamedi ECS 289G PAPER PRESENTATION - UC DAVIS 7
8 Architecture Response normalizaeon: AYer first and Second Layer Max Pooling: AYer both response normalizaeons and fiyh layer ReLU: AYer each layer Mohammad Motamedi ECS 289G PAPER PRESENTATION - UC DAVIS 8
9 OverfiQng Techniques to Reduce OverfiQng Mohammad Motamedi ECS 289G PAPER PRESENTATION - UC DAVIS 9
10 Data Augmenta6on Data is augmented by ExtracEng random patches Using both patches and their horizontal refleceon The same approaches is used in the test Eme (10 patches) Altering the intensity of RGB channels Add found principle components Emes a random variable proporeonal to the corresponding eigenvalue Effect Reduces the top 1 error by over 1% Mohammad Motamedi ECS 289G PAPER PRESENTATION - UC DAVIS 10
11 Dropout Sedng the output of each hidden neuron with probability of 0.5 This neuron is not effeceve in the forward path and does not play a role in the backpropagaeon. Reduces complex co- adapeon No neuron can rely on the presence of another neuron Mohammad Motamedi ECS 289G PAPER PRESENTATION - UC DAVIS 11
12 Implementa6on Training Eme: six days on two GTX GB GPUs Effect on network It is required to minimize the inter chip communicaeon AugmenEng the data on CPU in parallel with training on GPU Augmented data does not need to be stored on the disk Effect: Reduces the top 1 error by 1.7 % Reduces the top 5 error by 1.2 % Mohammad Motamedi ECS 289G PAPER PRESENTATION - UC DAVIS 12
13 Training Network is trained with stochasec gradient descent Weight decay: Momentum: 0.9 Weights are iniealized by random numbers from a zero mean Gaussian distribueon with standard deviaeon of 0.01 Divide learning rate by 10 when error stops improving Mohammad Motamedi ECS 289G PAPER PRESENTATION - UC DAVIS 13
14 Results Mohammad Motamedi ECS 289G PAPER PRESENTATION - UC DAVIS 14
15 Kernel values ater training Mohammad Motamedi ECS 289G PAPER PRESENTATION - UC DAVIS 15
16 ILSVRC 2010 Error (%) Top - 1 Top - 5 CNN SIFT + FVs [24] Sparse coding [2] Mohammad Motamedi ECS 289G PAPER PRESENTATION - UC DAVIS 16
17 ILSVRC Error (%) Model Top 1 Error (Val) Top 5 Error (test) SIFT + FVs [7] % 1 CNN 18.2% - 5 CNNs 16.4% 16.4% 7 CNNs 15.4% 15.3% 0 Top 5 SIFT + FVs [7] 5 CNNs 7 CNNs Mohammad Motamedi ECS 289G PAPER PRESENTATION - UC DAVIS 17
18 ILSVRC 2010 Mohammad Motamedi ECS 289G PAPER PRESENTATION - UC DAVIS 18
19 ILSVRC Mohammad Motamedi ECS 289G PAPER PRESENTATION - UC DAVIS 19
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