Reviewers are not allowed to ask for more experiments. You could signal this to the area chair. But ultimately, the paper probably won't be accepted.

Do write a rebuttal however, it's a great exercise.

More likely no, but if your rebuttal can change the reject it can go through. Spend a lot of effort on this reviewer.

Had orals recently with 4,4,4->5,5,4 and 4,4,4->5,5,5 (pre->post rebuttal). I think the strong accepts are a must, never had an oral without them.

Hello, I have been generating images with StyleGAN2-ada-pytorch in Google colab and I have my genereted images in google drive. I am struggling to find which seed-number an image is. Can someome please help me figure out how do I find the seed number to a specific image?

Thanks a lot!

At ICCV 2021, we got a paper accepted with initial reviews of borderline, borderline, weak reject.

If the reviewers' comments are addressable, and you do so in a good rebuttal, there is a chance for acceptance with a bit of luck.

It's my first submission too, and I'm an undergrad lol.
I've heard from my seniors and professors that changing a reject (1) to borderline (3) or weak accept (4) is difficult and that you need at least all borderlines to have a shot at getting accepted. They still told me to write it anyway for the experience.
Moreover, the confidence level of that reject is 4. The problem is that the reviewer asked for experimental results on an additional dataset, which I didn't work on. So, I'm not really sure how I can improve their score.
The other reviewers weren't too harsh with their reviews and I probably could have convinced them but I don't think I can convince reviewer #3 without quantitative results to back up my claims.

I mean... maybe we've invented AI not because machines are trainable but because humans are?

This was my first submission, I had a worse score than you but will write a rebuttal either ways (although I doubt I can convince everyone). Why wouldn't you? I'm not judging, asking out of curiosity as I don't know the "common practice" .

Same here

2 borderline 1 reject. If it wasn't for reviewer 3, I would've considered a rebuttal

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You can do Python locally tho

Hessian-free second order will not likely work. There are reasons why everyone using gradient descent. The only working second order method seems K-FAC (disclaimer - I have no first hand experience) but as you will use Julia you will have to implement it from scratch, and it's highly non-trivial (as you can expect from method which work where other failed)

Definitely yes. Your co-author will help you :)

2 borderline and 1 weak accept. I need to fight for it… wish me good luck

1 reject, 1 weak reject ans 1 borderline. Is there a chance during the rebuttal process ?

Flux.jl is the most popular deep learning library in Julia. I've played around with it a little, it's quite nice and easy to use. It is amazing how much more elegant the implementations become in julia compared to python.

There is also the less known Lux.jl package that is essentially an explicitly parameterized Flux (less mature than Flux though).

"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling"

https://fluxml.ai/Flux.jl/stable/models/recurrence/

Almost all of my analysis scripts are in matlab, so it would be easier to do things locally.

Can you remember the title of the paper?

Thanks for your reply…! You just brighten my day and give me the courage to response the reviewers… Thanks again!

I don't doubt your experience. Mine is totally different. I've never flipped one reviewer even thought I've always been polite and the reviewer clearly and objectively wrong. They just don't care. They have too much power. The AC just goes with the flow. This is not peer review, it's a joke.

This is not the case in ML conferences, according to my experience, it's a CV thing...

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The "entitlement" of reviewers is, quite often (I would say 50/50), a result of the authors' response. I have reviewed several papers where authors responded to fair comments by dismissing the reviewers and trying to make him feel dumb. That invariably ends up in reviewers not changing / lowering their rating.

Also, PhD students can be good reviewers.