2018 DS/ML digest 13
Blog posts / articles:
(0) Google notes on CNN generalization - https://goo.gl/XS4KAw
(1) Google to teaching robots in virtual environment and then trasferring models to reality - https://goo.gl/aAYCqE
(2) Google's object tracking via image colorization - https://goo.gl/xchvBQ
(2) Interesting articles about VAEs:
- A small intro into VAEs https://habr.com/company/otus/blog/358946/
- A small intuitive intro (super super cool and intuitive)
https://towardsdatascience.com/intuitively-understanding-variational-autoencoders-1bfe67eb5daf
- KL divergence explained
https://www.countbayesie.com/blog/2017/5/9/kullback-leibler-divergence-explained
- A more formal write-up http://arxiv.org/abs/1606.05908
- In (RU) https://habr.com/company/otus/blog/358946/
- Converting a FC layer into a conv layer http://cs231n.github.io/convolutional-networks/#convert
- A post by Fchollet https://blog.keras.io/building-autoencoders-in-keras.html
A good in-depth write-up on object detection:
- http://machinethink.net/blog/object-detection/
- finally a decent explanation of YOLO parametrization http://machinethink.net/images/object-detection/grid@2x.png
- best comparison of YOLO and SSD ever - http://machinethink.net/images/object-detection/architectures@2x.png
Papers with interesting abstracts (just good to know sich things exist)
- Low-bit CNNs - https://ai.intel.com/nervana/wp-content/uploads/sites/53/2018/06/ELQ_CameraReady_CVPR2018.pdf
- Automated Meta ML - https://arxiv.org/abs/1806.06927
- Idea - use ResNet blocks for boosting - https://arxiv.org/abs/1706.04964
- 2D-discrete-Fourier transform (2D-DFT) to encode rotational invariance in neural networks - https://arxiv.org/abs/1805.12301
- Smallify the CNNs - https://arxiv.org/abs/1806.03723
- BLEU review as a metric - conclusion - it is good on average to measure MT performance - https://www.mitpressjournals.org/doi/abs/10.1162/COLI_a_00322
"New" ideas in SemSeg:
- UNET + conditional VAE http://arxiv.org/abs/1806.05034
- Dilated convolutions for larget satellite images http://arxiv.org/abs/1709.00179 - looks like that this works only if you have high resolution with small objects
#digest
#deep_learning
Blog posts / articles:
(0) Google notes on CNN generalization - https://goo.gl/XS4KAw
(1) Google to teaching robots in virtual environment and then trasferring models to reality - https://goo.gl/aAYCqE
(2) Google's object tracking via image colorization - https://goo.gl/xchvBQ
(2) Interesting articles about VAEs:
- A small intro into VAEs https://habr.com/company/otus/blog/358946/
- A small intuitive intro (super super cool and intuitive)
https://towardsdatascience.com/intuitively-understanding-variational-autoencoders-1bfe67eb5daf
- KL divergence explained
https://www.countbayesie.com/blog/2017/5/9/kullback-leibler-divergence-explained
- A more formal write-up http://arxiv.org/abs/1606.05908
- In (RU) https://habr.com/company/otus/blog/358946/
- Converting a FC layer into a conv layer http://cs231n.github.io/convolutional-networks/#convert
- A post by Fchollet https://blog.keras.io/building-autoencoders-in-keras.html
A good in-depth write-up on object detection:
- http://machinethink.net/blog/object-detection/
- finally a decent explanation of YOLO parametrization http://machinethink.net/images/object-detection/grid@2x.png
- best comparison of YOLO and SSD ever - http://machinethink.net/images/object-detection/architectures@2x.png
Papers with interesting abstracts (just good to know sich things exist)
- Low-bit CNNs - https://ai.intel.com/nervana/wp-content/uploads/sites/53/2018/06/ELQ_CameraReady_CVPR2018.pdf
- Automated Meta ML - https://arxiv.org/abs/1806.06927
- Idea - use ResNet blocks for boosting - https://arxiv.org/abs/1706.04964
- 2D-discrete-Fourier transform (2D-DFT) to encode rotational invariance in neural networks - https://arxiv.org/abs/1805.12301
- Smallify the CNNs - https://arxiv.org/abs/1806.03723
- BLEU review as a metric - conclusion - it is good on average to measure MT performance - https://www.mitpressjournals.org/doi/abs/10.1162/COLI_a_00322
"New" ideas in SemSeg:
- UNET + conditional VAE http://arxiv.org/abs/1806.05034
- Dilated convolutions for larget satellite images http://arxiv.org/abs/1709.00179 - looks like that this works only if you have high resolution with small objects
#digest
#deep_learning