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deeplearning.ai Case Studies Why look at case studies?
Andrew Ng Outline Classic networks: • LeNet-5 ResNet Inception • AlexNet • VGG
deeplearning.ai Case Studies Classic networks
Andrew Ng LeNet - 5 ⋮ ⋮ " # 32×32 ×1 28×28×6 14×14×6 10×10×16 5×5×16 120 84 5 × 5 s = 1 f = 2 s = 2 avg pool 5 × 5 s = 1 avg pool f = 2 s = 2 [LeCun et al., 1998. Gradient-based learning applied to document recognition]
Andrew Ng AlexNet = ⋮ ⋮ 227×227 ×3 55×55 ×96 27×27 ×96 27×27 ×256 13×13 ×256 13×13 ×384 13×13 ×384 13×13 ×256 6×6 ×256 9216 4096 ⋮ 4096 11 × 11 s = 4 3 × 3 s = 2 MAX-POOL 5 × 5 same 3 × 3 s = 2 MAX-POOL 3 × 3 same 3 × 3 3 × 3 3 × 3 s = 2 MAX-POOL Softmax 1000 [Krizhevsky et al., 2012. ImageNet classification with deep convolutional neural networks]
Andrew Ng VGG - 16 224×224 ×3 CONV = 3×3 filter, s = 1, same MAX-POOL = 2×2 , s = 2 [CONV 64] ×2 224×224×64 POOL 112×112 ×64 [CONV 128] ×2 112×112 ×128 POOL 56×56 ×128 [CONV 256] ×3 56×56 ×256 POOL 28×28 ×256 [CONV 512] ×3 28×28 ×512 POOL 14×14×512 [CONV 512] ×3 14×14 ×512 POOL 7×7×512 FC 4096 FC 4096 Softmax 1000 [Simonyan & Zisserman 2015. Very deep convolutional networks for large-scale image recognition]
deeplearning.ai Case Studies Residual Networks (ResNets)
Andrew Ng Residual block ![#] ![#%&] '[#%(] = *[#%(] ![#] + ,[#%(] ![#%(] = -('[#%(]) '[#%&] = *[#%&]![#%(] + ,[#%&] ![#%&] = -('[#%&]) ![#%(] [He et al., 2015. Deep residual networks for image recognition]
Andrew Ng Residual Network x ![#] # layers training error Plain # layers training error ResNet [He et al., 2015. Deep residual networks for image recognition]
deeplearning.ai Case Studies Why ResNets work
Andrew Ng Why do residual networks work?
Andrew Ng ResNet Plain ResNet [He et al., 2015. Deep residual networks for image recognition]
deeplearning.ai Case Studies Network in Network and 1×1 convolutions
Andrew Ng Why does a 1 × 1 convolution do? 1 2 3 6 5 8 3 5 5 1 3 4 2 1 3 4 9 3 4 7 8 5 7 9 1 5 3 7 4 8 5 4 9 8 3 5 2 ∗ = ∗ = 6 × 6 6 × 6 × 32 1 × 1 × 32 6 × 6 × # filters [Lin et al., 2013. Network in network]
Andrew Ng Using 1×1 convolutions 28 × 28 × 192 28 × 28 × 32 ReLU CONV 1 × 1 32 [Lin et al., 2013. Network in network]
deeplearning.ai Case Studies Inception network motivation
Andrew Ng Motivation for inception network 28 × 28 × 192 1 × 1 3 × 3 5 × 5 MAX-POOL 128 32 32 64 28 28 [Szegedy et al. 2014. Going deeper with convolutions]
Andrew Ng The problem of computational cost 28 × 28 × 192 CONV 5 × 5, same, 32 28 × 28 × 32
Andrew Ng Using 1×1 convolution 28 × 28 × 192 CONV 1 × 1, 16, 1 × 1 × 192 28 × 28 × 16 CONV 5 × 5, 32, 5 × 5 × 16 28 × 28 × 32
deeplearning.ai Case Studies Inception network
Andrew Ng Inception module Previous Activation 1 × 1 CONV 1 × 1 CONV 3 × 3 CONV 1 × 1 CONV 5 × 5 CONV MAXPOOL 3 × 3,s = 1 same 1 × 1 CONV Channel Concat
Andrew Ng Inception network [Szegedy et al., 2014, Going Deeper with Convolutions]
Andrew Ng http://knowyourmeme.com/memes/we-need-to-go-deeper
MobileNet Convolutional Neural Networks
Andrew Ng Motivation for MobileNets • Low computational cost at deployment • Useful for mobile and embedded vision applications • Key idea: Normal vs. depthwise- separable convolutions [Howard et al. 2017, MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications]
Andrew Ng Normal Convolution Computational cost = #filter params x # filter positions x # of filters 6 x 6 x 3 * 3 x 3 x 3 = 4 x 4 4 x 4 x 5
Andrew Ng Depthwise Separable Convolution * = Normal Convolution * = * Depthwise Separable Convolution Depthwise Pointwise
Andrew Ng Computational cost = #filter params x # filter positions x # of filters 6 x 6 x 3 * 3 x 3 = 4 x 4 x 3 Depthwise Convolution
Andrew Ng Depthwise Separable Convolution * = Depthwise Convolution Pointwise Convolution * =
Andrew Ng Pointwise Convolution Computational cost = #filter params x # filter positions x # of filters * = 4 x 4 x 3 1 x 1 x 3 4 x 4 x 5 4 x 4
Andrew Ng Depthwise Separable Convolution * = Normal Convolution * = * Depthwise Separable Convolution Depthwise Pointwise
Andrew Ng Cost Summary [Howard et al. 2017, MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications] Cost of depthwise separable convolution Cost of normal convolution
Andrew Ng Depthwise Separable Convolution Depthwise Convolution * = Pointwise Convolution * =
MobileNet Architecture Convolutional Neural Networks
MobileNet Andrew Ng [Sandler et al. 2019, MobileNetV2: Inverted Residuals and Linear Bottlenecks] MobileNet v1 MobileNet v2 Depthwise Projection Expansion Residual Connection
Andrew Ng MobileNet v2 Bottleneck [Sandler et al. 2019, MobileNetV2: Inverted Residuals and Linear Bottlenecks] Residual Connection Expansion Depthwise Pointwise
MobileNet Andrew Ng [Sandler et al. 2019, MobileNetV2: Inverted Residuals and Linear Bottlenecks] MobileNet v1 MobileNet v2 Depthwise Projection Expansion Residual Connection
Andrew Ng MobileNet v2 Full Architecture [Sandler et al. 2019, MobileNetV2: Inverted Residuals and Linear Bottlenecks] conv2d conv2d 1 x 1 avgpool 7 x 7 conv2d 1 x 1
EfficientNet Convolutional Neural Networks
Andrew Ng [Tan and Le, 2019, EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks] Baseline Higher Resolution Deeper Wider EfficientNet ො 𝑦 ො 𝑦 ො 𝑦 ො 𝑦 Compound scaling ො 𝑦
deeplearning.ai Practical advice for using ConvNets Transfer Learning
deeplearning.ai Practical advice for using ConvNets Data augmentation
Andrew Ng Common augmentation method Mirroring Random Cropping Rotation Shearing Local warping …
Andrew Ng Color shifting +20,-20,+20 -20,+20,+20 +5,0,+50
Andrew Ng Implementing distortions during training
deeplearning.ai Practical advice for using ConvNets The state of computer vision
Andrew Ng Data vs. hand-engineering Two sources of knowledge • Labeled data • Hand engineered features/network architecture/other components
Andrew Ng Tips for doing well on benchmarks/winning competitions Ensembling • Train several networks independently and average their outputs Multi-crop at test time • Run classifier on multiple versions of test images and average results
Andrew Ng Use open source code • Use architectures of networks published in the literature • Use pretrained models and fine-tune on your dataset • Use open source implementations if possible

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