3 votes
Accepted

Does the term "data augmentation" imply increasing the training dataset?

I'm not familiar with any "authoritative" single definition somewhere, or not sure who used the term first, but I would personally indeed agree with the reviewer you mention. In fact I've ...
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  • 9,316
3 votes

How does rotating an image and adding new 'rotated classes' prevent overfitting?

How can data augmentation reduce overfitting? You write that you can already maybe see how data augmentation can help prevent overfitting in general, but it sounds a bit uncertain and it's still ...
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  • 9,316
3 votes

Would this relatively small dataset be enough to train a CNN?

Your data set would be what is called "unbalanced' and this can lead to problems in developing an accurate classifier. The best thing to do (which you might not be able to do) is to find more ...
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  • 614
2 votes
Accepted

What is the effect of training a neural network with randomly generated fake data that satisfies certain constraints?

This is not advisable. If you train your model with random data your model is not learning anything useful, because there is no information to gain from those examples. Even worse it may (and likely ...
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1 vote

What is the effect of training a neural network with randomly generated fake data that satisfies certain constraints?

If you add fake samples to the training set, your Neural Network learns new dataset that you just made, your fake samples are estimations so you add noise to your training set. you can use Leave one ...
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1 vote
Accepted

How to label edited images after data augmentation?

Yes, you should label it the same. But more importantly you need to make sure that each perturbation of the image doesn't change some important character of the image. Consider training an apple ...
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1 vote
Accepted

What exactly is data augmentation?

Data augmentation typically refers to the creation of new (training) data/instances (e.g. images) by e.g. modifying existing training/instances data in order to avoid over-fitting and improve the ...
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  • 32.9k
1 vote

Train Validation Test Splitting After or Before Data Augmentation?

I personally can’t think of any good reason to apply data augmentation before splitting the dataset, though one may exist. The issue is that if you augment first and split later, you risk introducing ...
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  • 36
1 vote

Train Validation Test Splitting After or Before Data Augmentation?

In my personal experience, that depends. Augmenting data for training purposes is valid, and can even improve performance, as you may be aware. For testing purposes, it may be valid. Let me give you ...
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1 vote

Is creating dataset only by augmentation a bad practice?

Data augmentation is usually rotating, cropping and translating images. And this makes sense if your network could meet these kind of images. If I take a digit recognition like LeNet, it is useless to ...
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  • 211
1 vote

Should one rescale (normalize) image before or after data augmentation?

So far, it seems this is more a software "integration" issue. One great tip from http://karpathy.github.io/2019/04/25/recipe/ is to visualize everything as often as you can during ...
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1 vote
Accepted

What is the difference between feature extraction with or without data augmentation?

There are two ways that you could perform data augmentation: Up front, by expanding the input dataset into a larger one, performing a range of changes to each input then storing the result. This ...
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  • 23.1k
1 vote
Accepted

Should I remove the text overlaying some images in the dataset before training the CNN?

Removing the overlayed text might increase accuracy, but you'd need to train a different model to do this, and that is an entirely different task as it is no longer classification, but generation. ...
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  • 1,324
1 vote
Accepted

Do I need to rotate the masks, if I also rotate the images and the masks are generated from the input?

Yes! This is crucial. If you rotate your input images for segmentation, you need to rotate the output masks as well. Otherwise the loss of your network will not be correctly calculated and your ...
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  • 600

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