5 ms·

You're clearly disillusioned with the general accessibility of ML research, but I don't think your cynicism is warranted here. Take a look at their prior works

by ansk 5y ago

You're clearly disillusioned with the general accessibility of ML research, but I don't think your cynicism is warranted here. Take a look at their prior works[1], and I think you'll agree they go above and beyond in making their work accessible and reproducible. There is no reason to doubt the open-source release of this work will be any different. As to why the release is delayed, I'd speculate it's because they put a significant additional amount of work into releases and because releasing code in a large corporation is a bureaucratic hassle. [1] https://nvlabs.github.io/stylegan2/versions.html https://nvlabs.github.io/stylegan2/versions.html

sillysaurusx 5y ago
There is no reason to doubt the open-source release of this work will be any different. 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. -- 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.
- phreeza 5y ago
  Expecting fully executable code to accompany every publication is kind of unique to the modern ML research Scene. As someone from a very different computational research field, where zero code is the norm, not the exception, this reads as a somewhat entitled rant. Reimplementation of a paper is actually a test of the robustness of the results. If you download the code of a previous paper, there may be some assumptions hidden in the implementation that aren't obvious. So I would argue that simply downloading and re-executing the author's implementation does not constitute reproducible research. I know it is costly, but for actual reproduction, reimplementation is needed.
  - sillysaurusx 5y ago
    I wasn't sure whether to post my edit as a separate comment or not, but I significantly expanded my comment just now, that helps explain my position. 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/ 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."
    - 5y ago
varispeed 5y ago
When I was writing a paper I had to include all source code in a state to be published, otherwise it wouldn't be accepted. I guess today the bar is much lower.
- godelski 5y ago
  What was released was a pre-print, not a publication. Every top ML conference requires releasing of code, but this is not the norm for CS research nor for research in general. The bar isn't low, this is just a pre-print.

by ansk 5y ago

sillysaurusx 5y ago

There is no reason to doubt the open-source release of this work will be any different. 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. -- 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.

phreeza 5y ago
Expecting fully executable code to accompany every publication is kind of unique to the modern ML research Scene. As someone from a very different computational research field, where zero code is the norm, not the exception, this reads as a somewhat entitled rant. Reimplementation of a paper is actually a test of the robustness of the results. If you download the code of a previous paper, there may be some assumptions hidden in the implementation that aren't obvious. So I would argue that simply downloading and re-executing the author's implementation does not constitute reproducible research. I know it is costly, but for actual reproduction, reimplementation is needed.
- sillysaurusx 5y ago
  I wasn't sure whether to post my edit as a separate comment or not, but I significantly expanded my comment just now, that helps explain my position. 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/ 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."
  - 5y ago

varispeed 5y ago

When I was writing a paper I had to include all source code in a state to be published, otherwise it wouldn't be accepted. I guess today the bar is much lower.

godelski 5y ago
What was released was a pre-print, not a publication. Every top ML conference requires releasing of code, but this is not the norm for CS research nor for research in general. The bar isn't low, this is just a pre-print.