xuebinqin
commented
3 years ago Yes, that's true. Actually, we did use MSRA2.5k as the validation. But all the other test sets have different characteristics from MSRA2.5K. Good performance on MSRA2.5K won't guarantee better generalizations on different datasets. We then remove the validation set and train to converge. If you want, you can plot the -log(loss). The flat curve of -log(loss) should make more sense because the loss decreases very slight in the later training stage and -log(loss) is able to amplifying the decreasing trend, which provides you more recognizable changes in the loss.
On Mon, Apr 26, 2021 at 12:20 PM Francesco Pochetti < @.***> wrote:
Hi, in section 4.3 of the paper https://arxiv.org/pdf/2005.09007.pdf you write:
We train the network until the loss converges without using validation set
From that sentence, I understand that you wait for the training loss to flatten out (converge), without using any validation set to check for over/underfitting. Am I getting this right? Thanks and have a great one!
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FraPochetti
Hi, in section 4.3 of the paper you write:
From that sentence, I understand that you wait for the training loss to flatten out (converge), without using any validation set to check for over/underfitting. Am I getting this right? Thanks and have a great one!