ReadingGroup

Getting involved

To register your interest, to get announcements, etc. from me about it, send me an email on djduff@itu.edu.tr

Proposed Schedule

The below schedule is only proposed, and subject to change.

• Week of 11 Sept: Foley & Maitlin Chapter 6: Distance & Size Perception
• Week of 18 Sept: Saxena, Min & Ng: Make3D
• Week of 25 Sept: Michels, Saxena & Ng: High speed obstacle avoidance
• Week of 02 Octr: Karsch, Liu & Kang: Depth Transfer
• Week of 09 Octr: LeCun, Bottou, Bengio & Haffner: CNNs
• Week of 16 Octr: Krizhevsky, Sutskever & Hinton: ImageNet/AlexNet
• Week of 23 Octr: Simonyan & Zisserman: VGG-16
• Week of 30 Octr: Break
• Week of 06 Novr: Eigen, Puhrsch & Fergus: Depth map prediction
• Week of 13 Novr: Shelhamer, Long & Darrell: Fully Convolutional Segmentation
• Week of 20 Novr: He, Zhang, Ren & Sun: ResNet
• Week of 27 Novr: Girshick, Donahue, Darrell & Malik: R-CNN
• Week of 04 Decr: Liao, Huang, Wang, Kodagoda, Yu & Liu: Fuse with laser
• Week of 11 Decr: Giuisti et al.: Forest trails CNN
• Week of 18 Decr: Break
• Week of 25 Decr: Cao, Wu & Shen: Fully convolutional depth 1
• Week of 01 Jany: Laina et al.: Fully convolutional depth 2
• Week of 08 Jany: Li, Klein & Yao: Fully convolutional depth 3
• Week of 15 Jany: Goodfellow et al.: Generative Adversarial Nets
• Week of 22 Jany: Dosovitskiy, Springenberg, Tatarchenko & Brox: Generating images
• Week of 29 Jany: Break
• Week of 06 Febr: Oord et al.: PixelCNN

Details

Foley & Maitlin Chapter 6 - Distance & Size Perception

https://books.google.com.tr/books?id=jLBmCgAAQBAJ&printsec=frontcover Go to Chapter 6.

Saxena, Min & Ng: Make3D

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4531745

Michels, Saxena & Ng: High speed obstacle avoidance

http://dl.acm.org/citation.cfm?id=1102426

Karsch, Liu & Kang: Depth Transfer

http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5551153 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6787109

LeCun, Bottou, Bengio & Haffner: CNNs

http://ieeexplore.ieee.org/abstract/document/726791/

Krizhevsky, Sutskever & Hinton: ImageNet/AlexNet

https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

Simonyan & Zisserman: VGG-16

http://arxiv.org/abs/1409.1556

Eigen, Puhrsch & Fergus: Depth map prediction

https://www.cs.nyu.edu/~deigen/depth/

Shelhamer, Long & Darrell: Fully Convolutional Segmentation

http://arxiv.org/abs/1605.06211

He, Zhang, Ren & Sun: ResNets

https://arxiv.org/abs/1512.03385

Girshick, Donahue, Darrell & Malik: R-CNN

https://arxiv.org/abs/1311.2524

Giuisti et al.: Forest trails CNN

http://ieeexplore.ieee.org/document/7358076/

See also youtube.

Cao, Wu & Shen: Fully convolutional depth 1

http://arxiv.org/abs/1605.02305

Laina et al.: Fully convolutional depth 2

http://arxiv.org/abs/1606.00373

Li, Klein & Yao: Fully convolutional depth 3

http://arxiv.org/abs/1607.00730

Goodfellow et al.: Generative Adversarial Nets

https://papers.nips.cc/paper/5423-generative-adversarial-nets

Dosovitskiy, Springenberg, Tatarchenko & Brox: Generating images

https://arxiv.org/abs/1411.5928

Oord et al.: PixelCNN

http://arxiv.org/abs/1606.05328