Jan 04, 2018 · One compelling reason for using cross-entropy over dice-coefficient or the similar IoU metric is that the gradients are nicer. The gradients of cross-entropy wrt the logits is something like p − t, where p is the softmax outputs and t is the target. Meanwhile, if we try to write the dice coefficient in a differentiable form: 2 p t p 2 + t 2 ...
19.08.2019 · With cross-entropy, at least some predictions are made for all classes: I initially thought that this is the networks way of increasing mIoU (since my understanding is that dice loss optimizes dice loss directly). However, mIoU with dice loss is 0.33 compared to cross entropy´s 0.44 mIoU, so it has failed in that regard.
04.01.2018 · The main reason that people try to use dice coefficient or IoU directly is that the actual goal is maximization of those metrics, and cross-entropy is just a proxy which is easier to maximize using backpropagation. In addition, Dice coefficient performs better at class imbalanced problems by design:
Feb 25, 2020 · A Far Better Alternative to Cross Entropy Loss for Boundary Detection Tasks in Computer Vision. ... By leveraging Dice loss, the two sets are trained to overlap little by little. As shown in Fig.4 ...
2 Scheduling of Weighted Cross Entropy and Weighted Dice Loss In segmentation task, the dice score is often the metric of importance. A loss function that directly correlates with the dice score is the weighted dice loss. But often the network trained with only weighted dice loss gets stuck in a local optima and doesn’t converge at all.
12.08.2019 · CrossEntropy could take values bigger than 1. I am actually trying with Loss = CE - log (dice_score) where dice_score is dice coefficient (opposed as the dice_loss where basically dice_loss = 1 - dice_score. I will wait for the results but some hints or help would be really helpful Megh_Bhalerao (Megh Bhalerao) August 25, 2019, 3:08pm #3
Jun 10, 2018 · Explore combination of cross-entropy and dice coefficient loss #3. Explore combination of cross-entropy and dice coefficient loss. #3. Closed. 3 tasks. daniel-j-h opened this issue on Jun 10, 2018 · 1 comment. Closed. 3 tasks. Explore combination of cross-entropy and dice coefficient loss #3.
We prefer Dice Loss instead of Cross Entropy because most of the semantic segmentation comes from an unbalanced dataset. Let me explain this with a basic ...
01.03.2020 · In cross entropy loss, the loss is calculated as the average of per-pixel loss, and the per-pixel loss is calculated discretely, without knowing whether its …
By considering a false negative and false positive, the output value drops even more in case of using Dice but the cross entropy stays smooth (i.e., Dice value ...
6. We prefer Dice Loss instead of Cross Entropy because most of the semantic segmentation comes from an unbalanced dataset. Let me explain this with a basic example, Suppose you have an image of a cat and you want to segment your image as cat (foreground) vs not-cat (background). In most of these image cases you will likely see most of the ...
Aug 12, 2019 · CrossEntropy could take values bigger than 1. I am actually trying with Loss = CE - log (dice_score) where dice_score is dice coefficient (opposed as the dice_loss where basically dice_loss = 1 - dice_score. I will wait for the results but some hints or help would be really helpful. Megh_Bhalerao (Megh Bhalerao) August 25, 2019, 3:08pm #3.
29.12.2021 · In this paper, we propose to use dice loss in replacement of the standard cross-entropy objective for data-imbalanced NLP tasks. Dice loss is based on the Sørensen--Dice coefficient or Tversky index , which attaches similar importance to false positives and false negatives, and is more immune to the data-imbalance issue.
We prefer Dice Loss instead of Cross Entropy because most of the semantic segmentation comes from an unbalanced dataset. Let me explain this with a basic example, Suppose you have an image of a cat and you want to segment your image …