The loss function of "LA/Baseline-RawNet2"

[Amforever]

As i read from "the read document of pytorch", the "CrossEntropyLoss() function" is criterion combintion of nn.LogSoftmax() and nn.NLLLoss(). Why there is still a nn.LogSoftmax() layer in the last layer of RawNet() and if i remove the nn.LogSoftmax() there would be Bad results.

[TakHemlata]

Hi,

Before I was not aware that CCE PyTorch includes logsoftmax() function. So I wrote externally.

I also checked by removing log-softmax from model definition and that's gave around similar performance (~10% EER) and 2021 ASVspoof baseline performance was 9.50% EER. I assume this difference might be due to cuda version.

Thanks

[Amforever]

Thanks, I had test RawNet() on ASVspoof2019 LA dataset and achieved around 4.30% EER on Eval set. In addiiton, I find the learning rate (lr) has great impact on the EER, when i set the lr=0.005, the RawNet was not convergenced and got the 100% EER.
Actually, I still doubt the principle of end-to-end anti-spoofing systems directly using WAV, even though the RawNet can get pretty results, thanks.

[TakHemlata]

Hi, ASVspoof 2021 challenge keys (ground truths) are now available on asvspoof website. You can download ASVspoof 2021 evaluation ground truths directly from asvspoof website : https://www.asvspoof.org/ Thanks, Regards, Hemlata

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On Tue, Mar 8, 2022 at 5:09 PM undefined_XD ***@***.***> wrote: Thanks, I had test RawNet() on ASVspoof2019 LA dataset and achieved around 4.30% EER on Eval set. In addiiton, I find the learning rate (lr) has great impact on the EER, when i set the lr=0.005, the RawNet was not convergenced and got the 100% EER. Actually, I still doubt the principle of end-to-end anti-spoofing systems directly using WAV, even though the RawNet can get pretty results, thanks. I'm trying to test my model on ASVspoof 2019 LA, but I have no idea that where i can get the ground truth of eval set. Can you tell me how i can get it ? THANKS ! — Reply to this email directly, view it on GitHub <#4 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AKPZY6532CIICQNPXAUYLZDU653TFANCNFSM5HDTLPWA> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>. You are receiving this because you commented.Message ID: ***@***.***>