added arxiv

app.py

@@ -122,7 +122,7 @@ abstract="<center> <img src='https://www.eml-unitue.de/publications/BayesCap/Bay
 
 method = "In this demo, we show an application of BayesCap on top of SRGAN for the task of super resolution. BayesCap estimates the per-pixel uncertainty of a pretrained computer vision model like SRGAN (used for super-resolution). BayesCap takes the ouput of the pretrained model (in this case SRGAN), and predicts the per-pixel distribution parameters for the output, that can be used to quantify the per-pixel uncertainty. In our work, we model the per-pixel output as a <a href='https://en.wikipedia.org/wiki/Generalized_normal_distribution'>Generalized Gaussian distribution</a> that is parameterized by 3 parameters the mean, scale (alpha), and the shape (beta). As a result our model predicts these three parameters as shown below. From these 3 parameters one can compute the uncertainty as shown in <a href='https://en.wikipedia.org/wiki/Generalized_normal_distribution'>this article</a>. <br><br>"
 
-closing = "For more details, please find the <a href='https://arxiv.org/'>ECCV 2022 paper here</a>."
+closing = "For more details, please find the <a href='https://arxiv.org/pdf/2207.06873.pdf'>ECCV 2022 paper here</a>."
 
 description = abstract + method + closing