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Is it possible to increase the precision to 100%? #1
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Thanks for your interest in our papers. For a specific dataset/model, you can plot the Accuracy-Rejection curves (fig.7 in OpenMix paper) or the Risk-Coverage curves, and find the rejection rate where the corresponding accuracy is 100% (or the risk is 0). To achieve exact 100% accuracy, the rejection rate might be high, and this depends on the dataset and model. |
Thanks for your kind reply. But my curiosity is can your methods help to achieve this goal (100% acc with sacrificing recall rate).
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From: ***@***.***>
Date: Thu, Aug 10, 2023 21:16 PM
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Subject: Re: [Impression2805/Awesome-Failure-Detection] Is it possible toincrease the precision to 100%? (Issue #1)
Thanks for your interest in our papers. For a specific dataset/model, you can plot the Accuracy-Rejection curves (fig.7 in OpenMix paper) or the Risk-Coverage curves, and find the rejection rate where the corresponding accuracy is 100% (or the risk is 0). To achieve exact 100% accuracy, the rejection rate might be high, and this depends on the dataset and model.
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Hello~ Our recent work SURE:SUrvey REcipes for building reliable and robust deep networks(CVPR2024) also focus on Failure Prediction(MisD) task. |
Hi~ Recently, I have read some paper of yours (OpenMix, FMFP) about MisD or OOD. I am curious about whether it is possible to increase the precision to 100% by sacrificing some recall rate after using some related confidence techniques.
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