There's a lot of subtletly with class weighting and neural networks - really neat empirical paper by @zacharylipton that looked at it: https://arxiv.org/pdf/1812.03372.pdf …
How to spot an event that only occurs 0.18% of the time? Here's an essential technique that is used more rarely than it should: class weighting. A must for dealing with very imbalanced classification datasets, such as those you see in fraud detection.https://keras.io/examples/structured_data/imbalanced_classification/ …
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+1. Ofcourse like with anything, caveats exist because it can get tricky tho. Under-represented classes in training can deviate from test (or in real world) by virtue of being, under-represented. Weights need to be carefully calibrated, and tools enabling that is
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This is such an essential concept that is needed for neural networks. With any type of medical dataset, you need to understand that a lot of data is imbalanced, which you need to adjust appropriately for in your
#NeuralNetwork.@TensorFlow has an article:https://www.tensorflow.org/tutorials/structured_data/imbalanced_data …Thanks. Twitter will use this to make your timeline better. UndoUndo
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Understanding this is very important as all "real" world datasets are imbalanced.
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Be careful though. If your batch size is small relative to the frequency of the minority class you will get irregular sizes of your gradients (e.g. small, small, small, huge gradient -> weight update).
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When working with images, usually modifying sampling strategy using duplicate samples + image augmentations >>> class weighting. Faster convergence, less overfitting.
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Using a better loss works often better than class weighting as far as my experience goes (for example FocalLoss). Would be nice to read about these things in a comparative study if it exists.
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Would this work with a segmentation task?
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