One way to reduce overfitting is by automatically augmenting your data.
Think about this: if you had an infinite number of samples, you would never overfit because your model would see every possibility out there.
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Think about this: if you had an infinite number of samples, you would never overfit because your model would see every possibility out there.
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Data augmentation is a way to generate more data using an existing dataset.
For example, by applying small transformations to existing images, you can generate many useful variations.
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For example, by applying small transformations to existing images, you can generate many useful variations.
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Here are some examples of possible variations that you could generate for an image:
▫️ Zoomed-in
▫️ Randomly cropped
▫️ Horizontally shifted
▫️ Horizontally flipped
▫️ Slightly rotated
▫️ More illuminated
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▫️ Zoomed-in
▫️ Randomly cropped
▫️ Horizontally shifted
▫️ Horizontally flipped
▫️ Slightly rotated
▫️ More illuminated
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Assuming we have a dataset with 1,000 images, if we augment it with 6 variations for every image, we will end up with 6,000 images.
This is quite an improvement!
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This is quite an improvement!
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Of course, size doesn't matter. Quality does.
We need to ensure that the data we generate is valid and exposes the model to a realistic distribution of real samples.
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We need to ensure that the data we generate is valid and exposes the model to a realistic distribution of real samples.
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One thing to keep in mind is that data augmentation is useful to reduce overfitting, but it may not be enough.
It really doesn't matter how many images you auto-generate if all of them are just a remix of the existing data.
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It really doesn't matter how many images you auto-generate if all of them are just a remix of the existing data.
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If we could generate completely different, real-looking images for any dataset out there, things will get really interesting.
We aren't there yet, but if you follow me, I'll let you know as soon as we make it 😎.
7/7
We aren't there yet, but if you follow me, I'll let you know as soon as we make it 😎.
7/7
You can use GANs for some use cases, But they aren't still a good general solution.
For example, generating real-looking photos of any object in random locations is far from an easy problem to solve.
For example, generating real-looking photos of any object in random locations is far from an easy problem to solve.
https://twitter.com/ricsinaruto/status/1377626603488960523?s=20
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