Data augmentation techniques for small image datasets?

Question

Currently i am training small logo datasets similar to Flickrlogos-32 with deep CNNs. For training larger networks i need more dataset, thus using augmentation. The best i\'m do

Answer 1

A good recap can be found here, section 1 on Data Augmentation: so namely flips, random crops and color jittering and also lighting noise:

Krizhevsky et al. proposed fancy PCA when training the famous Alex-Net in 2012. Fancy PCA alters the intensities of the RGB channels in training images.

Alternatively you can also have a look at the Kaggle Galaxy Zoo challenge: the winners wrote a very detailed blog post. It covers the same kind of techniques:

• rotation,
• translation,
• zoom,
• flips,
• color perturbation.

As stated they also do it "in realtime, i.e. during training".

For example here is a practical Torch implementation by Facebook (for ResNet training).

Answer 2

I've collected a couple of augmentation techniques in my masters thesis, page 80. It includes:

• Zoom,
• Crop
• Flip (horizontal / vertical)
• Rotation
• Scaling
• shearing
• channel shifts (rgb, hsv)
• contrast
• noise,
• vignetting