![]() ![]() I agree that KL would be preferred in practice here- just wanted to bring up a possible incongruity between the definition and naming. I am slightly inclined towards aligning with KL here because of how it is used in ML. unsqueeze( 1), v)Ĭlass LabelSmoothingCrossEntropy2( _Loss): full( size =( N, num_classes), fill_value = eps / num_classes, device = device). full( size =( N, 1), fill_value = 1 - eps, device = device) loss import _Loss def smooth_labels( target: Tensor, num_classes: int, eps: float = 0.1):ĭevice = target. functional as F from torch import Tensor from torch. The smoothed target can be the result of mixup/cutmix OR can be the result of a default smooth_labels method similar to what you described here: ![]() In order to achieve this we will need a modified loss which accepts an already smoothed target. These are SOTA primitives that we would like to add on TorchVision (see pytorch/vision#3911). The above covers a vast majority of applications but unfortunately it won't do for Computer Vision applications that use Data Augmentation techniques such as mixup and cutmix. eps = eps def forward( self, input: Tensor, target: Tensor) -> Tensor: loss import _Loss class LabelSmoothingCrossEntropy( _Loss):ĭef _init_( self, eps: float = 0.1, size_average = None, reduce = None, reduction: str = 'mean'): It's a middle ground solution with reasonable performance since it does not have to convert the target value to a one-hot encoded vector while at the same time does not introduce more complex parameters on the low level C++ code: You can have a simple wrapper such as the following that accepts a target value and reuses standard building blocks from PyTorch. I wonder if that justifies providing a more user-friendly wrapper that simplifies the code and gives the requested functionality.Īs you highlighted in the past, there are various potential implementations for this and each offers various degrees of flexibility/performance. Most solutions, require quite a significant amount of boilerplate code and careful implementations. There are numerous tickets with several followers, forum posts and discussions around this. Having said that, I wonder if the fact that there is so much confusion in the community hints that we have a UX problem. Generalized Entropy Regularization or There’s Nothing Special about Label Smoothing - section 2.1.Regularizing Neural Networks by Penalizing Confident Output Distributions - section 3.2.Rethinking the Inception Architecture for Computer Vision - section 7. ![]() For those, who would like more info about the relationship between Label Smoothing and Kullback-Leibler divergence, here are some references: Unfortunately there is much confusion around this (see #7455 (comment), #7455 (comment), etc), so hopefully your detailed analysis will provide proof that indeed KLDivLoss can support label smoothing. However, the entropic quantity we have defined is very useful in defining whether a given reaction will occur.First of all thanks for taking the time to write a comprehensive explanation of the situation. It is evident from our experience that ice melts, iron rusts, and gases mix together. This apparent discrepancy in the entropy change between an irreversible and a reversible process becomes clear when considering the changes in entropy of the surrounding and system, as described in the second law of thermodynamics. ![]()
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