UT-Austin-RPL
How
I am currently making a graduation project based on your thesis, so I am eager to know the specific epochs of your work. You can also reply me by email [email protected]. Extremely grateful！
But the result of GIGA-Aff is higher than the paper.
That's possible, different devices and different random seeds can give different results. I suggest running more tests with more different random seeds. BTW, how much higher?
Yes, the random seeds are [0, 1, 2, 3, 4]. The epoches are 20. I tested twice, the results were both higher than the paper.
emm, the result of GIGA-Aff sometimes are better than GIGA. About 1 percent.
---Original--- From: "Zhenyu @.> Date: Wed, Oct 27, 2021 06:42 AM To: @.>; Cc: @.>;"State @.>; Subject: Re: [UT-Austin-RPL/GIGA] How (Issue #8)
Hmmm, how about the result of GIGA? Is it better than GIGA-Aff?
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Hmmm, that's weird. Have you checked the loss curve and made sure they both converged?
This is the loss graph of GIGA-Aff training 20 epoches
This is the 10 epoches
Hi,
I trained GIGA-Aff for 10 epoches, the result is higher 5 percentage points than the paper. I wonder if there is a problem with the code.
So the GIGA trained with the same number of epochs perform worse than GIGA-Aff?
yes
---Original--- From: "Zhenyu @.> Date: Sun, Oct 31, 2021 05:10 AM To: @.>; Cc: @.>;"State @.>; Subject: Re: [UT-Austin-RPL/GIGA] How (Issue #8)
So the GIGA trained with the same number of epochs perform worse than GIGA-Aff?
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packed. Is the cause of network instability?
---Original--- From: "Zhenyu @.> Date: Mon, Nov 1, 2021 03:11 AM To: @.>; Cc: @.>;"State @.>; Subject: Re: [UT-Austin-RPL/GIGA] How (Issue #8)
Hmmm, that's weird. What scenario are you using? Packed or pile?
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Not sure about that. GIGA should perform better than GIGA-Aff, especially in packed scenarios.
Sorry to reply you now.
It's weird. I retrained giga-aff, and the result was lower than before, even lower than the paper.
on the different computer
---Original--- From: "Zhenyu @.> Date: Wed, Nov 3, 2021 23:23 PM To: @.>; Cc: @.>;"State @.>; Subject: Re: [UT-Austin-RPL/GIGA] How (Issue #8)
Do you retrain with the same setting?
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The absolute value may vary because the test scenes can be different, but the relative performance between GIGA and GIGA-Aff should stay the same. However, you said GIGA is worse than GIGA-Aff previously, which is very weird. Did you train GIGA and GIGA-Aff on the same computer?
The absolute value may vary because the test scenes can be different, but the relative performance between GIGA and GIGA-Aff should stay the same. However, you said GIGA is worse than GIGA-Aff previously, which is very weird. Did you train GIGA and GIGA-Aff on the same computer?
Yes, I trained them on the same computer before. Training the same model on the same computer, the loss curve obtained is different.
The absolute value may vary because the test scenes can be different, but the relative performance between GIGA and GIGA-Aff should stay the same. However, you said GIGA is worse than GIGA-Aff previously, which is very weird. Did you train GIGA and GIGA-Aff on the same computer?
Yes, I trained them on the same computer before. Training the same model on the same computer, the loss curve obtained is different.
The loss curve can be different. I think training on different computers is OK. The important thing is testing on the same computer so that after fixing the random seed, the generated scenes will be the same. (I should have asked if you test them on the same computer, it was a typo.)
I tested them on the same computer.
---Original--- From: "Zhenyu @.> Date: Fri, Nov 5, 2021 23:23 PM To: @.>; Cc: @.>;"State @.>; Subject: Re: [UT-Austin-RPL/GIGA] How (Issue #8)
The loss curve can be different. I think training on different computers is OK. The important thing is testing on the same computer so that after fixing the random seed, the generated scenes will be the same. (I should have asked if you test them on the same computer, it was a typo.)
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Not sure why this happens. I'll look back to this and re-train by myself later.
thank you very much. 比心🙆，非常感谢！
---Original--- From: "Zhenyu @.> Date: Fri, Nov 5, 2021 23:43 PM To: @.>; Cc: @.>;"State @.>; Subject: Re: [UT-Austin-RPL/GIGA] How (Issue #8)
Not sure why this happens. I'll look back to this and re-train by myself later.
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