深度学习中的可能/可能类别 [英] Possible/maybe category in deep learning

问题描述

我有兴趣利用我在深度学习任务中拥有的一些部分标记的数据.我使用的是完全卷积的方法，而不是从标记区域中采样补丁.

I'm interested in taking advantage of some partially labeled data that I have in a deep learning task. I'm using a fully convolutional approach, not sampling patches from the labeled regions.

我有一些掩码可以勾勒出图像中明确正例的区域，但图像中未掩蔽的区域不一定是负的——它们可能是正的.有谁知道将此类课程纳入深度学习环境的方法?

I have masks that outline regions of definite positive examples in an image, but the unmasked regions in the images are not necessarily negative - they may be positive. Does anyone know of a way to incorporate this type of class in a deep learning setting?

Triplet/contrastive loss 似乎是要走的路，但我不确定如何适应模糊"或模糊的负/正空间.

Triplet/contrastive loss seems like it may be the way to go, but I'm not sure how to accommodate the "fuzzy" or ambiguous negative/positive space.

推荐答案

尝试标签平滑，如深度学习书籍:

我们可以假设对于一些小的常数 eps，训练集标签 y 是正确的，概率为 1 - eps，否则任何其他可能的标签都可能是正确.

We can assume that for some small constant eps, the training set label y is correct with probability 1 - eps, and otherwise any of the other possible labels might be correct.

标签平滑基于带有 k 个输出值的 softmax 正则化模型，方法是用 eps/k 和 1 - (k - 1)/的目标替换硬 0 和 1 分类目标k * eps，分别.

Label smoothing regularizes a model based on a softmax with k output values by replacing the hard 0 and 1 classification targets with targets of eps / k and 1 - (k - 1) / k * eps, respectively.

查看我关于在 Pandas 中实现标签平滑的问题.

否则，如果您确定某些领域是负面的，有些领域是正面的，而有些领域是不确定的，那么您可以引入第三个不确定类.我处理过包含不确定类的数据集，这些类对应于可能属于任何可用类的样本.

Otherwise if you know for sure, that some areas are negative, other are positive while some are uncertain, then you can introduce a third uncertain class. I have worked with data sets that contained uncertain class, which corresponded to samples that could belong to any of the available classes.

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