<p>I've collected a couple of augmentation techniques in <a href="https://arxiv.org/pdf/1707.09725.pdf#page=94" rel="noreferrer">my masters thesis, page 80</a>. It includes:</p>

<ul>
<li>Zoom,</li>
<li>Crop</li>
<li>Flip (horizontal / vertical)</li>
<li>Rotation</li>
<li>Scaling</li>
<li>shearing</li>
<li>channel shifts (rgb, hsv)</li>
<li>contrast</li>
<li>noise,</li>
<li>vignetting</li>
</ul>
Data augmentation techniques for small image datasets?
<p>Currently i am training small logo datasets similar to <a href="http://www.multimedia-computing.de/flickrlogos/" rel="noreferrer">Flickrlogos-32</a> with deep CNNs. For training larger networks i need more dataset, thus using augmentation. The best i'm doing right now is using affine transformations(featurewise normalization, featurewise center, rotation, width height shift, horizontal vertical flip). But for bigger networks i need more augmentation. I tried searching on kaggle's national data science bowl's <a href="https://www.kaggle.com/c/datasciencebowl/forums/t/11360/data-augmentation" rel="noreferrer">forum</a> but couldn't get much help. There's code for some methods given <a href="https://github.com/benanne/kaggle-galaxies/blob/master/realtime_augmentation.py" rel="noreferrer">here</a> but i'm not sure what could be useful. What are some other(or better) image data augmentation techniques that could be applied to this type of(or in any general image) dataset other than affine transformations? </p>