图像增强会使性能变差 [英] Image augmentation makes performance worse

问题描述

我正在研究图像分割项目，并且一直在尝试采用图像增强技术来增加训练集的大小.刚开始，我只是尝试使用水平翻转将图像大小加倍，但是我发现性能要比不使用它差很多.是否有任何可以共享的见解.谢谢.

I am working on an image segmentation project, and have been trying to adopt the image augmentation technique to increase the training set size. At first, I just try to use the horizontal flip to double the image size, but I found the performance was much worse than not using it. Are there any insight that can be shared. Thanks.

推荐答案

因此，基本上，您需要回答一个重要问题:翻转后的图片在您的域中是否是有效图片?

So basically you need to answer yourself one important question: Is a flipped image a valid image in your domain?

1. 如果不是这样，则可能仅由于您向网络提供的无效输入而可能会损害您的培训过程，而无效输入可能会在数据中学习网络的虚假模式.翻转可能会损害您的训练的情况并不少见-例如在徽标识别中，重要的是不要更改数据的方向以正确学习徽标.

2. 如果是，则可能有不同原因导致模型开始表现更差的负载.其中之一可能是它的容量太小，无法学习数据中的所有模式.其次-您没有足够的示例-当您添加翻转的图像时，事实证明它实际上记录了您的交易案例的负载.另一件事是，也许您学习的时间太短，将迭代次数设置为更大的值可能是个好主意.

1. If not - then it may harm your training process simply because you are providing a network an invalid input which may learn your network spurious patterns in your data. It's not so rare that flips might harm your training - e.g. in logo recognition it's important to not change the orientation of your data in order to learn logos correctly.

2. If yes - then there might be loads of different reason why your model started to behave worse. One of them might be that it has simply too small capacity and it's not able to learn all the patterns in your data. Second - that you have not enough examples - and when you add the flipped image it turned out that it in fact memoized loads of your traning cases. Another thing is that maybe you learnt it for a too small amount of time and setting the number of iterations to a bigger value might be a good idea.

可以肯定的是-由于翻转的数据有效，因此您的模型不能很好地概括.

One thing is sure - your model is not generalizing well since your flipped data is valid.

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