Lecture: Deep Convolutional Neural Networks

Transcription

1 Lecture: Deep Convolutional Neural Networks Shubhang Desai Stanford Vision and Learning Lab 1

2 Today s agenda Deep convolutional networks History of CNNs CNN dev Architecture search 2

3 Previously argmax c )*+, Input Image Feature Extractor Prediction y" Classifier Classification Output y CE L Input Label Loss Function Loss Value 3

4 Previously argmax c )*+, Input Image Feature Extractor Prediction y" Classifier Classification Output y CE L Input Label Loss Function Loss Value 4

5 Previously argmax c )*+, Input Image Feature Extractor Prediction y" Classifier Classification Output y CE L Input Label Loss Function Loss Value 5

6 Previously argmax c )*+, Input Image Feature Extractor Prediction y" Classifier Classification Output y CE L Input Label Loss Function Loss Value 3) Using gradient descent! 6

7 Previously Why only one convolution? argmax c )*+, Input Image Feature Extractor Prediction y" Classifier Classification Output y CE L Input Label Loss Function Loss Value 3) Using gradient descent! 7

8 Convolutions Convolutions = Insights More Convolutions = More Insights? 8

9 Recall Hubel and Weisel 9

10 Recall Hubel and Weisel The thing has edges The edges can be grouped into triangles and ovals The triangles are ears, the oval is a body It s a mouse toy! 10

11 Recall Hubel and Weisel The thing has edges The edges can be grouped into triangles and ovals The triangles are ears, the oval is a body It s a mouse toy! 11

12 Convolutions Across Channels Image Filter Output 12

13 Convolutions Across Channels Image Filter Output 13

14 Convolutions Across Channels more output channels = more filters = more features we can learn! Image Filter Output 14

15 Convolutions Across Channels Conv Block 15

16 Stacking Convolutions Conv Block Conv Block Conv Block Conv Block Input Output Output Output Output 16

17 Stacking Convolutions Conv Block Conv Block Conv Block Conv Block Input Output Output Output Output 17

18 Convolutional Neural Networks (ConvNets) Neural networks which involve the stacking of multiple convolutional layers to produce output Often times end in fully-connected layers as the classifier 18

19 History of ConvNets LeNet

20 History of ConvNets AlexNet

21 History of ConvNets NiN

22 History of ConvNets Inception Network

23 Why Do They Work So Well? 23

24 Why Do They Work So Well? 24

25 Why Do They Work So Well? 25

26 Why Do They Work So Well? 26

27 Why Do They Work So Well? This is the neural network s receptive field it s able to see! 27

28 Great Applications of ConvNets Fine-Grained Recognition Staffordshire Bull Terrier Segmentation Art Generation Facial Recognition Ranjay Krishna 28

29 What is CNN Dev? Define the objective What is the input/output? What is the loss/objective function? Create the architecture How many conv layers? What size are the convolutions? How many fully-connected layers? Define hyperparameters What is the learning rate? Train and evaluate How did we do? How can we do better? 29

30 What is CNN Dev? Define the objective What is the input/output? What is the loss/objective function? Create the architecture How many conv layers? What size are the convolutions? How many fully-connected layers? Define hyperparameters What is the learning rate? Train and evaluate How did we do? How can we do better? Can this be automated? 30

31 Neural Architecture Search Automatically finds the best architecture for a given task Before we had to find best featurizer for a fixed classifier now we find the best classifier and featurizer in tandem! 31

32 In summary We can use convolutions as a basis to build powerful visual systems We can leverage deep learning to automatically learn the best ways to do previously difficult tasks in computer vision Still lots of open questions! If you re interested in machine learning and/or deep learning, take: Machine Learning (CS 229) Deep Learning (CS 230) NLP with Deep Learning (CS 224n) Convolutional Neural Networks (CS 231n) 32