First of all let me start by saying a big thank you for the great work you have been doing with DLS. It is an amazing piece of software that really helps prototype DL models and I am certain it will push the DL (and other) community to the right direction.
In the first webinar I attended I alluded to the possibility of bringing popular deep learning networks into DLS. This would be very helpful for users that are not as comfortable with model development as the expert part of the community (myself included). I think it can be done in two ways, from what I can see. First, the community of users, as it grows, develops, prototypes, and shares their work with each other. I have no doubt this will happen in time. Second, is the DLS team itself provides a helping hand by developing a few of the popular models (or sometimes layers) within DLS.
I thought I would make this thread in order to have a place where users can add their items on the wishlist of models they would like to see implemented in DLS. I’ll start with a few of mine below (as a new user I can only add two links for the items below):
- Tensorflow research models has a lot of interesting, and usually bleeding edge, implementations
- Deep Markov Model for multiple time-series prediction (https://github.com/clinicalml/dmm)
- Any of the implementations of CapsuleNets found on GitHub
- Pix2PixHD Nvidia Model
- The many flavors of GAN models.
- Variational models and bayesian approaches, e.g. variation autoencoder
- Attention transfer (https://github.com/szagoruyko/attention-transfer) and attention layer (with context and without)
I hope this is not an unreasonable, or infeasible, matter to discuss. I realize that in some cases there might be some issues with intellectual property. However, the AI community has been developing at incredible speeds mostly due to the fact that the researchers around the world, and big companies, are more than willing to make their research and insights freely accessible. It’s an incredible property for a scientific field and one that could be used in this case.
Thank you very much for reading this far.