Then this is not a scientific contribution yet.
We must wait and see.
The most important tenet of science, is to doubt. I didn’t even read the name on the paper before I wrote my comment. Yes, I know this group. They’re why I got into ML, along with the group from OpenAI who published GPT-2. Because A+ science.
Their claims here are likely wrong unless and until proven otherwise. This isn’t a hardline position. It’s been my experience across many codebases, during my two years of trying to reproduce many ideas.
I agree that that is an example of A+ science. But why do you think they’re punishing this now, today? Either because conference deadline or because nVidia pressure. Neither of those are related to helping me achieve the scientific method: reproducing the idea in the paper, to verify their claims.
All I can do is kind of try to reverse engineer some vague claims in a pdf, without those things.
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Let me tell you a little bit about my job, because my time with my job may soon come to an end. I think that might clear up some confusion.
My job, as an ML researcher, is to learn techniques that may or may not be true, combine them in novel ways, and present results to others.
Knowledge, Contribution, Presentation, in that order.
The first step is to obtain knowledge. Let's set aside the question of why, because why is a question for me personally, which is unrelated.
Scientific knowledge comes when Knowledge, Contribution, and Presentation are all achieved in a rigorous way. The rigor allows people like me to verify that I have knowledge. Without this, I have mistaken knowledge, which is worse than useless. It's an illusion – I'm fooling myself.
When I got into ML two years ago, I thought that knowledge would come from reading scientific papers. I was wrong.
Most papers, are wrong. That's been my experience for the past two years. My experience may be wrong. Maybe others obtain rigorous scientific knowledge through the paper alone.
But researchers happen to obtain a dangerous thing: prestige. Unfortunately, prestige doesn't come from helping others obtain knowledge. It comes from that last step -- presentation.
The presentation on this thread is excellent. It's another Karras release. I agree; there's no reason to doubt they'll be just as rigorous with this release as they are with stylegan2.
But knowledge doesn't come from presentation. Only prestige.
Prestige makes a lot of new researchers try very hard to obtain the wrong things.
If all of these were small concerns, or curious quirks, they'd be a footnote in my field guide. But I submit that these things are front and center to the current state of affairs in 2021. Every time a release like this happens, it generates a lot of fanfare and we come together in celebration because ML Is Happening, Yay!
And then I try to obtain the Knowledge in the fanfare, and discover that either it's absent or mistaken. Because there are no tools for me to verify their claims -- and when I do, I often see that they don't work!
That's right. I kept finding out that these things being claimed, just aren't true. No matter how enticing the claim is, or whether it sounds like "Foobars are Aligned in the Convolution Digit," the claim, from where I was sitting, seemed to be wrong. It contained mistaken knowledge -- worse than useless.
Unfortunately, two years with no salary takes a toll. I could spend another few years doing this if I wanted to. But I wound up so disgusted with discovering that we're all just chasing prestige, not knowledge, that I'd rather ship production-grade software for the world's most boring commercial work, as long as the work seems useful and the team seems interesting. Because at least I'd be doing something useful.
I'd be very interested in your thoughts on that position, because if it's mistaken, I shouldn't be saying it. It represents whatever small contribution I can make to fellow new ML researchers, which is roughly: "watch out."
In short, for two years, I kept trying to implement stated claims -- to reproduce them in exactly the way you say here -- and they simply didn't work as stated.
It might sound confusing that the claims were "simply wrong" or "didn't work." But every time I tried, achieving anything remotely close to "success" was the exception, not the norm.
And I don't think it was because I failed to implement what they were saying in the paper. I agree that that's the most likely thing. But I was careful. It's very easy to make mistakes, and I tried to make none, as both someone with over a decade of experience (https://shawnpresser.blogspot.com/) and someone who cares deeply about the things I'm talking about here.
It takes hard work to reproduce the technique the way you're saying. I put all my heart and soul into trying to. And I kept getting dismayed, because people kept trying to convince me of things that either I couldn't verify (because verification is extremely hard, as you well know) or were simply wrong.
So if I sound entitled, I agree. When I got into this job, as an ML researcher, I thought I was entitled to the scientific method. Or anything vaguely resembling "careful, distilled, correct knowledge that I can build on."
There are always assumptions. At least with public code and models those assumptions are laid bare for all to see and potentially expose any bad assumptions.
To his defense, the spirit of his rant was valid, the letter made it sound entitled.
I'm in the middle of a PhD and this is always an issue. It takes awhile to learn how to read papers and to gather enough background knowledge that you can read between the lines (publications are limited, you can't put everything in a paper. This is why having code is so great, it accelerates the process). You're two years into your journey, this is often when things _start_ turning the other direction. There's a reason PhDs take so long, and that's with experts (hopefully) helping you learn how to read papers, telling you which papers to read (which is a challenge in of itself), having the ability to spend full time on learning, and learning how to build background knowledge on a subject while learning the state of the art. There's a reason ML pays the big bucks. It takes a long time to learn/gather expertise, it is fucking difficult, and it has direct applications that can lead to useful products today (a big component of why you get paid big bucks). It is also easy to lose track of your progress. I remember the first research paper I read was complete gibberish to me. I'm 3 years into my PhD and now I can understand papers in my niche. But for a long time a lot of stuff didn't click. This is normal. It takes time to learn and 2 years isn't that much (especially when you have a full time job). Making contributions in your first year of a PhD is atypical, even in your second year. It only happens at top universities where people have a lot of help and resources.
Research it hard. It takes years to become an expert and learn how to read papers. Don't give up, but calm down and recognize that given more time things will make more sense.
The bar isn't low, this is just a pre-print.
