...but, this is:
> What if, instead, data and queries can be represented as short natural language sentences, and queries can be answered from these sentences?
Useful for some things, but not useful for most things.
 - eg. https://www.matthewtancik.com/nerf
Tensorflow needs to stop breaking their entire codebase every 10 or so minor versions and also make their archived versions for all OS readily available. Do a pip3 install tensorflow==1.15 just for an illustration of what I am talking about.
There are hundreds if not thousands of extensive code TF projects on GitHub that are now almost completely useless because the libraries they require are not readily available. Sure you can use them as a template to translate into TF 2 but as a keeper of an important open source library, tensorflow team has been far less than diligent at making sure projects containing their code do not break as tensorflow pushes out updates.
But I certainly agree with the frustration with google breaking everything. If I wanted to use Chainer (or for that matter Keras) I would just use that. I don't need tensorflow to imitate it for me and break the alternative approach I was using.
A database (simplest case is an array or a dict) isn’t fundamentally different from a function in its interface. To both of them you submit a query/argument and they return some value. A “function” might “compute” the value after the query is submitted, while the database might “store” a pre-computed value. Especially in physical contexts where there is a natural continuity in the thing being represented (think object density/radiance, as opposed to student names), it makes sense to also have the option to interpolate among records in a database.
Neural networks (being generic learnable function approximators) are a nice framework to straddle this boundary for generic cases.
In the specific example of the Nerf, we use a neural network rather than discrediting space and assigning a value per pixel/voxel. A consequent advantage is that we can have an a data description that is adaptive non-uniform in its information density, unlike a voxel grid.
There’s more to say, but I hope this gives a feel :-)
In that case the system doesn't have transparency and control. Imagine you are given a fact that "Alice works at Google" and 1 month later you want to remove that and add "Alice works at Facebook". In fully latent representation model of the data, you can not guarantee this.
For similar reasons, you can not guarantee preventing implicit biases in the data. Pre-trained generative models tend to hallucinate facts, for example you have two facts: "Alice works at hospital", "Bob works at hospital", if someone asks what does Alice/Bob do? You don't want your model hallucinates Nurse/Doctor.
eg. That dinosaur skeleton is derived from 60 photos. The drumkit comes from ~100.
...so it's not magic, it's very close to what you get from standard photogrammetry. The big part of this is that it isn't representing the scene as block of voxels like some other approaches.
> The biggest practical tradeoffs between these methods are time versus space.
> LLFF produces a large 3D voxel grid for every input image, resulting in enormous storage requirements (over 15GB for one “Realistic Synthetic” scene).
> Our method requires only 5 MB for the network weights (a relative compression of 3000× compared to LLFF), which is even less memory than the input images alone for a single scene from any of our datasets.
Anyway, so... if you could do the same sort of thing with a similar accuracy to non-images for a 'neural representation database', that'd be pretty neat.
Either way, I'm struggling to understand how it might work. Care to say more?
How is it possible that the original submission has been on the front page for 8+ hours, and all discussion is focused on this completely unrelated link?
Have people stopped reading original submission links in favor of comments so much that the discussion is no longer related to the original submission at all?