I have a hard time being mad about people trying every different combination of everything, it's good to know if it works better or not. At some point however it's just bloat, and may make it more difficult to do research.

If I was trying to publish papers myself it might get to me.

3D morphable models are the way to go for image of face to 3d model. I have previously done a lot of research work on this. The deep learning model takes an image as input and outputs 3d model as an obj or fbx file. This can be used in any 3d editing software. Take a look at DECA on github.

It depends on the downstream application, what do you need to do with it?

>However there is a small Chance they acted in good faith and did Not see that the randomness in the GAN wont do anything.

Why is the default assumption malice?

Especially if the only benefit would be a workshop paper

>IMO reviewers at these journals/conferences need to be more mindful of this kind of plagiarism/low-effort submission.

Workshops in general have a very low bar, this surely wouldn't have been published in the main track.

Other than that I don't really see the point of this rant.

Yes there are a lot of bad papers, there are a lot of bad papers even in the main tracks, you just kind of get used to it.

It feels a lot like hitting down a well. Maybe these are some undergraduates doing their first research project and it's more about learning the methodologies and writing rather than very novel approaches.

Nerf networks

I'm looking for a solution that allows anyone to do this using just their phone

The bigger use isn’t games, but animation or VFX. They require high quality simulations that sometimes take days to render a few seconds of simulation. Every tech that can cut that time down without a substantial loss of quality is huge.

With unlimited money, I would use a sphere of high quality cameras on a metal rig to capture them at a specific instance in time. Then I'd use photogrammetry to stitch the images together.

No I didn’t lol

It's not plagiarism. Novelty and plagiarism are two separate concepts.

I'd really like to see more realistic ground (contact) physics with different textures and terrains. Someone might walk differently in a desert environments vs a forest environment vs a snow environment for example. If there's debris on the ground such as small rocks or other debris it may cause the character to adjust foot contact to compensate. Sloping features could also be incorporated and modeled. Walking is a big thing but vehicle movement in these environments is also something that can be drastically improved upon.

My point was that you said image classification has been around since before NNs. That is false. Image classification has only ever been done with NNs. Sometimes they are radically different than what is normally used today (e.g. RAMnets and WISARD), but they've always been NNs.

Yup. Nerf just replaced the construction step after you "register" all the camera positions using traditional algorithms. Usually via COLMAP.

Not saying that's a bad thing, existing algorithms are already good at estimating camera positions and parameters. It was the 3d reconstruction step that was previously lacking.

For anyone wanting to try this, I suggest using Nerf-W . The original Nerf required extremely accurate camera parameter estimates that you're not going to get with a cell camera and COLMAP. Nerf-w is capable of doing some fine adjustments as it runs. It even works decent reconstructing scenes using random internet photos.

The workflow is COLMAP to register the camera positions used to take the pictures and estimate camera parameters, then export those into the Nerf model. Most of the Nerf repos are already setup to make this easy.

This paper is a good overview of how to build a Nerf from random unaligned images. They did it using frames from a sitcom, but you could take a similar approach to Nerf almost anything https://arxiv.org/abs/2207.14279

I would more memory is more important. Buy the 3060 with the 12GB. If you have more money get the 3090 24GB. The memory is more important in my view because it will allow you to run bigger models.

Not sure if this helps, but you can use our hosted flan-t5 model at deepinfra.com using HTTP API. It's free for now. Disclaimer I work at deepinfra. If you want GPT-Neo or GPT-J I can deploy those also.

Did you do this? I hope you didnt do that.

Check out this great post that includes fine tuning Flan-T5, Language Models vs. The SAT Reading Test:

https://jeffq.com/blog/language-models-vs-the-sat-reading-test/

I actually have a 3060 too, in theory a 3060ti should be up to 30% faster, but most of the times the 3060 is fast enough and faster than any T4.

For making a few images on stable diffusion maybe the difference will be 15 vs 20 seconds, for running whisper on several hours of audio it could be 45 minutes vs 1 hour. The difference will only matter if the model is optimized to fully use the GPU in the first place.

Time to learn about Zellij

This remind me of the Best of Talking Heads album cover :)

Yeah, and DDR4 DIMMs are fairly inexpensive as compared to upgrading a GPU for more VRAM.

Hi guys,

I’m looking into machine learning and it’s use in shark conservation. The below figure shows the effectiveness of image classification of sharks.

Can anybody help me interpret this? The internet is telling me that if you follow two species to where they meet, the colour in the square represents how often one has been mistaken for the other. But if this is the case, why is there a uniform line down the middle showing a much higher number?

Thanks in advance from a confused biologist…

Normalized Confusion Matrix

it does look to be 20-100x slower for those huge models, but still bearable if you're the only user on the machine. Still better than nothing if you don't have lots of GPU memory.

Azure has cheap A100 spot instances. Only 1$ per hour per A100. Up until now I have always gotten my instances immediately and I have only been kicked out twice in over 100 training runs (each run lasts a couple hours). So I am very happy with it at the moment and would highly recommend it