1. Lecture 4b Data augmentation for CNN training

2. Data Augmentation Training of large network requires a lot of samples.
The easiest and most common method to enlarge the dataset using label-preserving transformations:
image translations
re-scale (both up and down) before crop
horizontal reflections
elastic deformation with random interpolations ((bilinear, area, nearest neighbor and cubic, with equal probability) (Simard, 2003)
photometric distortion and altering the intensities of the RGB channels in training images (A.G. Howard. Some improvements on deep convolutional neural network based image classification, 2013)

3. Plankton competition
The Plankton competition
classify grayscale images of plankton into one of 121 classes.
The images from camera were processed by a segmentation algorithm to isolate individual organisms, and then cropped.
The size of an organism in the resulting images is proportional to its actual size, and does not depend on the distance to the camera.

4. Plankton: preprocessing and data augmentation
Rescaling all images to the same size
zero mean unit variance (ZMUV) normalization
Data augmentation:
rotation: random with angle in [0°, 360°]
translation: random with shift in [-10 ;10] pixels
rescaling: random with scale factor [1/1.6 ; 1.6]
flipping
Shearing (parallel sliding of lines): random with angle [-20° , 20°]
stretching: random with stretch factor [1/1.3 ; 1.3]

5. Data Augmentation
preprocessed image
Augmented image

6. ConvNet with cyclic pooling
add rotation to data layer
4 parallel identical nets to process rotated images
root-mean-square pooling  before output layer

7. Pre-defined features Pre-defined features:
Image size (pixels)
Image moments
Texture features
These features were fed into 2 dense layers and combined the CNN features just before output ‘softmax’ layer

8. Test-time data transformations
For each individual model
Applied a quasi-random set of 70 transformations to input image
compute predictions across various augmented versions of the input image
averaged predictions