GoogLeNet Insights
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GoogLeNet introduced several key insights for designing efficient deep learning networks: 1. Exploit local correlations in images by concatenating 1x1, 3x3, and 5x5 convolutions along with pooling. 2. Decrease dimensions before expensive convolutions using 1x1 convolutions for dimension reduction. 3. Stack inception modules upon each other, occasionally inserting max pooling layers, to allow tweaking each module. 4. Counter vanishing gradients with intermediate losses added to the total loss for training deep networks. 5. End with a global average pooling layer instead of fully connected layers to avoid overfitting.
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GoogLeNet Insights
- 1. Five Insights from GoogLeNet You Could Use In Your Own Deep Learning Nets Auro Tripathy 3b 4a 4b 4c 4d 4e 5a3a 5b www.shaBerline.com 1
- 2. Year 1989 Kicked-Off ConvoluKon Neural Nets Ten-Digit Classifier using a Modest Neural Network with Three Hidden Layers Backpropaga)on Applied to Handwri4en Zip Code Recogni)on. LeCun, et. al. hBp://yann.lecun.com/exdb/publis/pdf/lecun-89e.pdf Hidden Units Connec-ons Params Out – H3 (FC) 10 Visible 10 x (30W +1B )= 310 10 x (30W +1B )= 310 H3 – H2 (FC) 30 30 * (192 Weights + 1 Bias) = 5790 30 * (192 W + 1 B) = 5790 H2 – H1 (Conv) 12 X 4 x 4 = 192 192 x (5 x 5 x 8 + 1)= 38592 5 x 5 x 8 x 12 + 192 Biases = 2592 H1 – Input (Conv) 12 x 8 x 8 = 768 768 x (5 x 5 x 1 + 1) = 19968 5 x 5 x 1 x 12 + 768 Biases = 1068 Totals 16 x 16 In + 990 Hidden + 10 Out 64660 ConnecKons 9760 Params Each of the units in H2 combines local informaKon coming from 8 of the 12 different feature maps in H1. www.shaBerline.com 2
- 3. Year 2012 Marked The InflecKon Point Reintroducing CNNs Led to Big Drop in Error for Image ClassificaKon. Since Then, Networks ConKnued to Reduce 28.2 25.8 16.4 11.7 7.3 6.7 3.57 0 5 10 15 20 25 30 ILSVRC'10 ILSVRC'11 ILSVRC'12 (Alexnet) ILSVRC'13 ILSVRC'14 ILSVRC'14 (GoogLeNet) ILSVRC'15 (ResNet) 0 20 40 60 80 100 120 140 160 Error % Layers www.shaBerline.com 3 Top-5
- 4. The Trend has been to Increase the number of Layers (& Layer Size) • The typical ‘design paBern’ for ConvoluKonal Neural Nets: – Stacked convoluKonal layers, • linear filter followed by a non-linear acKvaKon – Followed by contrast normalizaKon and max pooling, – PenulKmate layers (one or more) are fully connected layers. – UlKmate layer is a loss layer, possibly more than one, in a weighted mix • Use of dropouts to address the problem of over-fipng due to many layers • In addiKon to classificaKon, architecture good for localizaKon and object detecKon – despite concerns that max-pooling dilutes spaKal informaKon www.shaBerline.com 4
- 5. The Challenge of Deep Networks 1. Adding layers increases the number of parameters and makes the network prone to over-fipng – Exacerbated by paucity of data – More data means more expense in their annotaKon 2. More computaKon – Linear increase in filters results in quadraKc increase in compute – If weights are close to zero, we’ve wasted compute resources www.shaBerline.com 5
- 8. IntuiKon behind the IncepKon Module • Cluster neurons according to the correlaKon staKsKcs in the dataset – An opKmal layered network topology can be constructed by analyzing the correlaKon staKsKcs of the preceding layer acKvaKons and and clustering neurons with highly correlated outputs. • We already know that, in the lower layers, there exists high correlaKons in image patches that are local and near-local. – These can be covered by 1x1 convoluKons – AddiKonally, a smaller number of spaKally spread-out clusters can be covered by convoluKon over larger patches; i.e., 3x3, and 5x5 – And there will be decreasing number of patches over larger and larger regions. • It also suggests that the architecture is a combina)on of the of all the convoluKons, the 1x1, 3x3, 5x5, as input to the next stage • Since max-pooling has been successful, it suggests adding a pooling layer in parallel www.shaBerline.com 8
- 9. In Images, correlaKon tends to be local, exploit it. Heterogeneous set of convoluKons to cover spread-out clusters www.shaBerline.com 9 Cover very local clusters w/1x1 convoluKons Cover more spread-out clusters w/3x3 convoluKons Cover even more spread-out clusters w/5x5 convoluKons 5x5 3x3 1x1 5x5 3x31x1 Previous Layer
- 12. www.shaBerline.com 12 Insights… 3b 4a 4b 4c 4d 4e 5a3a 5b
- 13. GoogLeNet Insight #1 (Summary from previous Slides) Leads to the following architecture choices: • Choosing filter sizes of 1X1, 3X3, 5X5 • Applying all three filters on the same “patch” of image (no need to choose) • ConcatenaKng all filters as a single output vector for the next stage. • ConcatenaKng an addiKonal pooling path since pooling is essenKal to the success of CNNs. www.shaBerline.com 13
- 15. GoogLeNet Insight #3 Stack IncepKon Modules Upon Each Other • Occasionally insert max-pooling layers with stride 2 to decimate (by half) the resoluKon of the grid. • Stacking IncepKon Layers benefits the results when used at higher layers (not strictly necessary) – Lower layers are kept in tradiKonal convoluKons fashion (for memory efficiency reasons) • This stacking allows for tweaking each module without uncontrolled blowup in computaKonal complexity at later stages. – For example, a tweak could be increase width at any stage. www.shaBerline.com 15
- 16. GoogLeNet Components Stacking IncepKon Modules 3b 4a 4b 4c 4d 4e 5a3a 5b Input Average Pooling Traditional Convolutions (Conv + MaxPool + Conv + MaxPool) Linear Nine Inception Modules SoftMax w/LossMaxPool Label www.shaBerline.com 16
- 17. GoogLeNet Insight #4 Counter-Balancing Back-PropagaKon Downsides in Deep Networks • A potenKal problem – Back-propagaKng thru deep networks could result in “vanishing gradients” (possibly mean, dead ReLUs). • A soluKon – Intermediate layers do have discriminatory powers – Auxiliary classifiers were appended to the intermediate layers – During training, the intermediate loss was added to the total loss with a discounted factor of 0.3 www.shaBerline.com 17
- 18. Two AddiKonal Loss Layers for Training to Depth 3b 4a 4b 4c 4d 4e 5a3a 5b Input Average Pooling Traditional Convolutions (Conv + MaxPool + Conv + MaxPool) Linear Nine Inception Modules SoftMax w/Loss 2MaxPool Average Pooling 1x1 Conv DropOutFully Connected SoftMax w/Loss 0Linear Label SoftMax w/Loss 1 www.shaBerline.com 18
- 19. GoogLeNet Insight #5 End with Global Average Pooling Layer Instead of Fully Connected Layer • Fully-Connected layers are prone to over-fipng – Hampers generalizaKon • Average Pooling has no parameter to opKmize, thus no over-fipng. • Averaging more naKve to the convoluKonal structure – Natural correspondence between feature-maps and categories leading to easier interpretaKon • Average Pooling does not exclude the use of Dropouts, a proven regularizaKon method to avoid over-fipng. 3b 4a 4b 4c 4d 4e 5a3a 5b Global Average Pooling Linear Layer for adapting to other label Sets SoftMax w/Loss Label www.shaBerline.com 19
- 20. Summarizing The Insights 1. Exploit fully the fact that, in Images, correlaKon tend to be local • Concatenate 1X1, 3X3, 5x5 convoluKons along with pooling 2. Decrease dimensions wherever computaKon requirements increase via a 1X1 Dimension ReducKon Layer 3. Stack IncepKon Modules Upon Each Other 4. Counter-Balance Back-PropagaKon Downsides in Deep Network • Uses intermediate losses in the final loss 5. End with Global Average Pooling Layer Instead of Fully Connected Layer www.shaBerline.com 20