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Dear Zhang,
I am currently reviewing the source code related to your work on Out-of-Distribution (OOD) adversarial training and have encountered some questions regarding your method for evading OOD…
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First of all, thank you for your work.
The method is promising and your article is very interesting, so I tried to use it in two way:
- determining whether a detected object is a False Positive
- …
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**Description:**
I would like to propose the addition of a new loss function and detector to the pytorch-ood library: an Angular Loss function (e.g., ArcFace) and an Angle-Based Detector. These add…
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您好,我想请教一下你们,关于Tail & OOD detection 的实验设计思路,我可能基础较差,没有弄懂这个逻辑。我的理解是验证不确定性的泛化能力
okoys updated
2 years ago
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Hello, I trained cifar10-lt and cifar100-lt with the default parameters in the code, but the results I obtained are quite different from those in the paper.
Could you please tell me how can I achiev…
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Hello, thank you for your work, I have some questions about the dataset used when trying to reproduce the results of ImageNet-1k. I have downloaded ImageNet-1k as instructed, but I'm unsure about the …
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In the paper you mention you validate the hyperparameters for the input processing (the FGSM magnitude) and the feature ensemble using adversarial samples (right part of Table 2 in the paper). I think…
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Hello, this work has provided me with a lot of inspiration!
But I have some uncertainties regarding the evaluation criteria for out-of-distribution generalization.
Based on my understanding of out-o…
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Hi,
Thank you for your work on heatmap based OOD detection. Do you have the link to checkpoints after running ood_training.py for cifar10 and cifar100? Currently there are checkpoints only for pretr…
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Hi Lingkai
Thanks for sharing the great code for the fantastic paper.
I want to consult some questions if possible.
- 1 How to let the model say 'I don't know'?
> For OOD samples, it is wise…