Ever thought of going beyond TensorFlow, GPU or TPU to solve your image classification problems? From the standpoint of deep learning, the problem of image processing can be solved in a much better way with Transfer Learning. It is a computer vision method that helps develop accurate models while saving a lot of time. This presentation will help you find out why it is so beneficial? Agenda: The history of image processing What is Transfer Learning? Introduction to Convolutional Neural Networks (CNNs) Different types of CNN architectures like AlexNet, VGG, Inception, and ResNet Performance of various CNN architectures Solving a medical image diagnosis problem with the above-discussed architectures

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Our Agenda
01 Image Processing history
02 Different Approaches
03 Residual Neural Networks
04 Performances
05 Demo Application

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About Knoldus MachineX
MachineX is a group of data wizards.
We are a team of Data Scientist and engineers with a
product mindset who deliver competitive business
advantage.

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9. State-of-the-art Image Processing
across domains

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Image processing

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Image processing History
Traditional way

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Traditional Way
Traditional pipeline for image classification involves two
modules
● Feature extraction
● Classification

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Problems
The problem with this pipeline
● Feature extraction cannot be tweaked according to
the classes and images
● Completely different from how we humans learn to
recognize things.

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Image processing across
domains

15. 15 Healthcare

16. 16 manufacturing

17. Convolutional Neural Network
(CNN, or ConvNet)

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● Convolutional base
● Classifier

19. Transfer learning

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The Application of
skills, knowledge,
and/or attitudes that
were learned in one
situation to another
learning situation
transfer learning is usually
expressed through the use of
pre-trained models

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Problems
The problem was
● less learned rate in each generation
● Number of knowledge amount passed down was
less

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Difference

25. Understanding various architectures of
Convolutional Networks
ResNet, AlexNet, VGGNet, Inception

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ImageNet Large Scale Visual Recognition Challenge
(ILSVRC)
CNN architectures of ILSVRC
top competitors

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AlexNet
● 5 Convolutional (CONV) layers and 3 Fully Connected (FC) layers
● 62 million trainable variables

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AlexNet

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AlexNet
● Data augmentation is carried out to reduce overfitting
● Used Relu which achieved 25% error rate about 6 times faster
than the same network with tanh nonlinearity.
● AlexNet introduced Local Response Normalization (LRN) to
help with the vanishing gradient problem

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VGGNet
● VGG16 has a total of 138 million parameters
● Conv kernels are of size 3x3 and maxpool kernels are of size 2x2 with
stride of two

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VGGNet

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VGGNet
● It is painfully slow to train.
● Spatial pooling is carried out by five max-pooling layers, which
follow some of the conv. layers

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34. ResNet : Deep Residual learning

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Hierarchical Features and role of Depth
● Low, Mid , and High-level features
● More layers enrich the “levels” of the features
● Previous ImageNet models have depths of 16 and 30
layers

36. Is learning better networks as easy as
stacking more layers ?

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Adding layers to deep
Convolutional neural nets

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Construction Insight
● Consider a shallow architecture and its deeper
counterpart
● The deeper model would would just need to copy the
shallower model with identity mapping
● Construction solution suggests that a deeper model
should produce no higher training error that its shallow
counterpart

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Residual Functions
● We explicitly reformulate the layers as learning residual functions
with reference to the layer inputs, instead of learning
unreferenced functions
● H[x] = F[x] + x

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Residual vs Plain

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Experiment
● 152 layer Layers on ImageNet
○ 8* Deeper than VGGNet
○ Less parameters
● ResNet achieve 3.57% error on Imagenet test
○ 1st place in ILSVRC

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Results
● AlexNet and ResNet-152, both have about 60M parameters but there is
about 10% difference in their top-5 accuracy
● VGGNet not only has a higher number of parameters and FLOP as compared
to ResNet-152, but also has a decreased accuracy
● Training an AlexNet takes about the same time as training Inception (10
times less memory requirements)

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Clinic Assistant
● Notebook http://bit.ly/2D2LOQT
● Web App https://virtual-clinic.onrender.com

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References
● [1]. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional
neural networks. In Advances in neural information processing systems,pages 1097–1105,2012.
● [2]. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. arXiv preprint
arXiv:1512.03385,2015.
● [3]. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image
recognition. arXiv preprint arXiv:1409.1556,2014.
● [4]. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A.
Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition,pages 1–9,2015.
● https://arxiv.org/pdf/1901.06032.pdf

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Thank You