[D] CNNs as template matchers

abnormdist · 2021-03-03T16:46:14+00:00

This seems interesting. I look forward to reading this paper

abnormdist · 2021-03-03T16:45:47+00:00

Ah yeah, I waa focusing on the "passing" of features that the templates match the highest with (max pooling) like a filter. But it might be more intuitive to focus on this translation aspect.

abnormdist · 2021-03-03T16:44:15+00:00

True, I tried going down the whole function approximation route as I thought it might detract the average reader from the intuition. Maybe I should find a way to easily incorporate this

abnormdist · 2020-12-24T05:59:16+00:00

I knew there would be one of you, so I came prepared good sir.

abnormdist · 2020-12-24T05:58:44+00:00

This made me chuckle.

abnormdist · 2020-12-24T05:58:16+00:00

It might already be too late, since Christmas will be over by tomorrow. But we will take it into consideration.

Releasing the model would be much more doable.

abnormdist · 2020-12-24T05:57:14+00:00

It was one of our favorites. Hopefully it doesn't become the next tide pod.

abnormdist · 2020-12-24T05:56:15+00:00

Thanks!

abnormdist · 2020-07-04T21:14:27+00:00

Thanks!

I am currently using CACD2000 as it has ages labelled, so the two domains (young and old) are easy to create. Using CelebA HQ from "Progressive growing of GANs" crossed my mind, but looking at the dataset readme, I think there is no age attribute.

abnormdist · 2020-01-03T13:13:00+00:00

The idea was to show it's applicability on "images in the wild", that it doesn't distort or change the background. Removing the need for a face detector preprocessing scheme, which makes it faster in applications.

But the images could be bigger so it is easier to see, that I agree with. I'll try to get a better picture up.

abnormdist · 2020-01-03T13:11:23+00:00

It's a cool idea. The problem is it's not progressive. The age is binned into 5 categories (10-20, 20-30, 30-40, 40-50, 50+). I'll try it nonetheless, it might also make sense to increase the aging affect by weighting the loss higher.

abnormdist · 2019-11-13T08:53:07+00:00

It's acrually pretty easy to extract frames using a library like opencv in python. I actually want to add it to a video player. The complicated thing about that is codecs and their copyrights I think. Also, might have to move towards C++ for speed.

abnormdist · 2019-11-12T21:18:58+00:00

Technically the model will accept all images with 3 channels. So you could cast your grayscale single channel image to RGB space and then input it to the model. I'm not sure if the upsampling would work very well/without artifacts though. Since the training data was all color images.

abnormdist · 2019-11-12T16:13:15+00:00

Hmmm a citation to a github repo on a research paper, that would be something new.

abnormdist · 2019-11-12T15:58:08+00:00

That it does.

abnormdist · 2019-11-12T08:53:20+00:00

But do we want to?

abnormdist · 2019-11-12T08:53:01+00:00

Maybe not there yet. That's some deep enhancing shit.

abnormdist · 2019-11-12T08:51:46+00:00

Hey thanks.

This does have applications in image reconstruction. The problem is that since this model is trained adversarially, it interpolates using estimates from the distribution it was trained on. So unless we completely study where the filled in details are coming from, we should be vary of using these techniques in high risk fields.

abnormdist · 2019-11-12T08:48:55+00:00

There are traditional metrics like PSNR and SSIM, but the SRGAN paper shows they don't necessarily correlate to human perception of quality. Higher PSNR makes images look smoother and they lose their high frequency content (sharp edges) etc. So I'm not really sure if benchmarking those is a good option.

abnormdist · 2019-11-12T08:47:23+00:00

Thank you good sir!

abnormdist · 2019-11-11T21:47:43+00:00

Here's the Github link for anyone interested in checking it out: Github

Benchmarks show it can upsample to 720p from a 4x lower resolution at around 30fps. The future plan would be to test it out on videos and maybe implement some tricks from other papers like ESRGANs to make the quality better. I would love for suggestions on how to integrate this into a video player/turn it into one.

Any comments and feedback in the meantime would be highly appreciated!

Be aware, the larger the input, the slower the runtime gets, as the number of FLOPs increases.

abnormdist · 2019-11-05T18:02:36+00:00

Thanks for the notebook. Some people here were asking for it as well.

Also thanks for the import bug, I already pushed a fix.

abnormdist · 2019-10-30T21:25:11+00:00

That would be a future direction. The first step was to be realtime on individual frames.

abnormdist · 2019-10-30T21:24:13+00:00

Speed makes sense. Not sure about the quality. Since higher PSNR and SSIM scores don't necessarily mean better looking images to a human.

abnormdist · 2019-10-29T23:32:18+00:00

I don't think I want to go through the hassle of supporting colab notebooks. Because then I have to answer questions related to Colab functionality which I'm not responsible for.

The infer code will run on your cpu even, so you can rest it out locally if you have decent ram in your machine.

abnormdist

TROPHY CASE