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Steering by Flashcards

screen-shot-2016-09-08-at-11-01-50-pm

screen-shot-2016-09-13-at-9-02-56-am

“To remove a bias towards driving straight the training data includes a higher proportion of frames that represent road curves”

 “to build a CNN to do lane following we only select data where the driver was staying in a lane and discard the rest. We then sample that video at 10 FPS.”

screen-shot-2016-09-13-at-10-02-37-am

CNN for:

  • Object-detection
  • Segmentation
  • Human pose estimation
  • Video classification
  • Object tracking
  • Superresolution

 

Image Links:

  1. https://arxiv.org/abs/1604.07316
  2. http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w3/papers/Gurghian_DeepLanes_End-To-End_Lane_CVPR_2016_paper.pdf
  3. https://www.ptgrey.com/case-study/id/10846
  4. http://net-scale.com/doc/net-scale-dave-report.pdf
  5. http://repository.cmu.edu/cgi/viewcontent.cgi?article=2874&context=compsci
  6. http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w3/papers/Gurghian_DeepLanes_End-To-End_Lane_CVPR_2016_paper.pdf
  7. https://drive.google.com/a/bench.co/file/d/0B9raQzOpizn1TkRIa241ZnBEcjQ/view
  8. https://culurciello.github.io/tech/2016/06/04/nets.html
  9. https://github.com/commaai/research/blob/master/SelfSteering.md
  10. https://research.googleblog.com/2016/08/improving-inception-and-image.html
  11. https://research.googleblog.com/2016/08/tf-slim-high-level-library-to-define.html
  12. https://github.com/tensorflow/models/blob/master/slim/deployment/model_deploy.py
  13. http://download.visinf.tu-darmstadt.de/data/from_games/
  14. https://github.com/tensorflow/models/tree/master/slim#fine-tuning-a-model-from-an-existing-checkpoint
  15. https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim
  16. https://github.com/tensorflow/models/blob/master/slim/README.md
  17. https://github.com/tensorflow/models/blob/master/slim/slim_walkthough.ipynb
  18. http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43022.pdf
  19. http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43442.pdf
  20. http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/44903.pdf
  21. http://arxiv.org/pdf/1409.1556.pdf
  22. https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
  23. https://arxiv.org/abs/1512.03385

Resources:

  1. https://github.com/rwightman/tensorflow-litterbox
  2. https://github.com/tensorflow/models
  3. https://github.com/tensorflow/models/tree/master/slim
  4. https://github.com/facebook/fb.resnet.torch
  5. https://github.com/rbgirshick/py-faster-rcnn/
  6. https://github.com/KaimingHe/deep-residual-networks
  7. https://github.com/daijifeng001/mnc
  8. https://github.com/facebookresearch/multipathnet
  9. https://github.com/jcjohnson/neural-style
  10. https://www.quora.com/How-does-deep-residual-learning-work
  11. http://videolectures.net/deeplearning2016_montreal/
  12. http://academictorrents.com/details/743c16a18756557a67478a7570baf24a59f9cda6
  13. http://cs231n.github.io/
  14. http://www.deeplearningbook.org/
  15. http://cilvr.nyu.edu/doku.php?id=deeplearning:slides:start
  16. http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html
  17. https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/

 

 

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