Looking at the first thing (GANs): developed by Ian Goodfellow, who is now at OpenAI. The first linked paper (https://arxiv.org/pdf/1605.05396v2.pdf) is out of University of Michigan and Max Planck Institute.
Google Brain/DeepMind/FAIR/MS Research do great work, but there is plenty of great work elsewhere too.
For example, just yesterday an implementation[1] of Fast Layer Normalization for TensorFlow was posted on Reddit[2] but someone, who says they don't really do C++ ("I am really new to CUDA and C++"). This can speed up training (sometimes) by 5-10X (!). That's democratization.
No? Goodfellow has a PhD from UMontreal (one of the deep learning academic powerhouses), advised by Bengio. He then joined Google Research and then left for OpenAI (which has a billion dollars in funding and is backed by YC/Elon Musk).
The first author of that paper has a PhD from MIT and is a postdoc at Berkeley. Berkeley is also an academic ML powerhouse, and receives millions per year in industry funding just for ML. The first two authors are also former Microsoft Research interns.
Looking at the first thing (GANs): developed by Ian Goodfellow, who is now at OpenAI. The first linked paper (https://arxiv.org/pdf/1605.05396v2.pdf) is out of University of Michigan and Max Planck Institute.
The second (https://phillipi.github.io/pix2pix/) is out of Berkeley, the third (https://arxiv.org/pdf/1609.04802v3.pdf) is out of Twitter (which I guess counts as a rich American tech company).
Google Brain/DeepMind/FAIR/MS Research do great work, but there is plenty of great work elsewhere too.
For example, just yesterday an implementation[1] of Fast Layer Normalization for TensorFlow was posted on Reddit[2] but someone, who says they don't really do C++ ("I am really new to CUDA and C++"). This can speed up training (sometimes) by 5-10X (!). That's democratization.
[1] https://github.com/MycChiu/fast-LayerNorm-TF
[2] https://www.reddit.com/r/MachineLearning/comments/5gt0wm/p_f...