Fei-Fei Li & Justin Johnson & Serena Yeung
Lecture 12 - May 16, 2017
Fei-Fei Li & Justin Johnson & Serena Yeung
Lecture 12 - May 16, 2017
1
Lecture 12:
Visualizing and Understanding
Fei-Fei Li & Justin Johnson & Serena Yeung
Lecture 11 - May 10, 2017
Fei-Fei Li & Justin Johnson & Serena Yeung
Lecture 11 - May 10, 2017
2
Administrative
Milestones due tonight on Canvas, 11:59pm
Midterm grades released on Gradescope this week
A3 due next Friday, 5/26
HyperQuest deadline extended to Sunday 5/21, 11:59pm
Poster session is June 6
Fei-Fei Li & Justin Johnson & Serena Yeung
Lecture 11 - May 10, 2017
3
Last Time: Lots of Computer Vision Tasks
Classification
+ Localization
Semantic
Segmentation
Object
Detection
Instance
Segmentation
CAT
GRASS, CAT,
TREE, SKY
DOG, DOG, CAT DOG, DOG, CAT
Single Object
Multiple Object
No objects, just pixels
This image is CC0 public domainThis image is CC0 public domain
Fei-Fei Li & Justin Johnson & Serena Yeung
Lecture 11 - May 10, 2017
4
This image is CC0 public domain
Class Scores:
1000 numbers
What’s going on inside ConvNets?
Input Image:
3 x 224 x 224
What are the intermediate features looking for?
Krizhevsky et al, “ImageNet Classification with Deep Convolutional Neural Networks”, NIPS 2012.
Figure reproduced with permission.
Fei-Fei Li & Justin Johnson & Serena Yeung
Lecture 11 - May 10, 2017
5
First Layer: Visualize Filters
AlexNet:
64 x 3 x 11 x 11
ResNet-18:
64 x 3 x 7 x 7
ResNet-101:
64 x 3 x 7 x 7
DenseNet-121:
64 x 3 x 7 x 7
Krizhevsky, “One weird trick for parallelizing convolutional neural networks”, arXiv 2014
He et al, “Deep Residual Learning for Image Recognition”, CVPR 2016
Huang et al, “Densely Connected Convolutional Networks”, CVPR 2017
