They say there are a lot of different ways. The weight contribution of each instance to the loss of value with the inverse frequency class. That is, each instance of the smaller class contributes more where the larger class instances pay less tribute to the final loss.
and?
Many different ways. Two of them are solid and easy to implement.
Weight contribution of each instance to loss value with inverse class frequency. That is, each instance of the smaller class contibute more where as larger class instances tribute less to finall loss. In that way, we prevent larger classes to overexposure their being to network representation. To ease the implementationt, you can even weight learning rate per class. It is not exactly the same thing on papet but works in practice.
Suppose you train your network with N instances per batch. You sample each instance in your batch by the inverse frequency of the class. Thus instances in larger classes has small probability to be selected. Due to population differences of the classes final batch is equally distibuted.
Both work fine for imbalanced situations. Try both to find the best for you. You can also be interested to read my post on this topic
http://www.erogol.com/fighting-class-unbalance-supervised-ml-problem/
Regards
Many different ways. Two of them are solid and easy to implement.
Weight contribution of each instance to loss value with inverse class frequency. That is, each instance of the smaller class contibute more where as larger class instances tribute less to finall loss. In that way, we prevent larger classes to overexposure their being to network representation. To ease the implementationt, you can even weight learning rate per class. It is not exactly the same thing on papet but works in practice.
Suppose you train your network with N instances per batch. You sample each instance in your batch by the inverse frequency of the class. Thus instances in larger classes has small probability to be selected. Due to population differences of the classes final batch is equally distibuted.
Both work fine for imbalanced situations. Try both to find the best for you. You can also be interested to read my post on this topic
http://www.erogol.com/fighting-class-unbalance-supervised-ml-problem/
Regards
Here are some links that might help!
https://towardsdatascience.com/deep-learning-unbalanced-training-data-solve-it-like-this-6c528e9efea6
https://stats.stackexchange.com/questions/134599/how-to-classify-a-unbalanced-dataset-by-convolutional-neural-networks-cnn
We could do it by weight loss calculation in each class. .....it is worth almost all types of data.
There are many ways to deal with this issue, but I will only mention a few of them. The method that should be used highly depends on the data you are using. Also, the highly imbalanced data could mean that you only have one or two examples for some classes, while for others you have hundreds or even thousands. I assume that we are talking about a classification problem.
Convolutional Neural Networks (CNNs) require a lot of data to train, so if you have only a few examples for some classes you can either get rid of them, as the CNN will not be able to learn them anyway, or you can multiply them. The first technique is self explainable so I will not get into more details. For the second technique, there are several options. You can just create some 'copies' for these data (which is easier than using weighted training methods) or you can use data augmentation/ manipulation techniques (e.g. for images you can tilt the image, rotate it or even flip it) to expand the data patterns for some of the classes.
Another technique could be (if you have at least a few thousands of data patterns per class) to create a balanced dataset (subsampled), where all classes will have the same number of data patterns (for both train and test datasets).
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