An example of visual data augmentation techniques used in machine learning (IMAGE)
Caption
An example of visual data augmentation techniques used in machine learning, which captures the main principle of variability effects: exposure to variation along non-discriminative dimensions (i.e., by rotating, changing the color, or partially masking the target image) improves the neural networks' ability to generalize (in this case - to identify a fox), but at the cost of slowing down initial learning. Humans show a similar effect: more variable input is harder to learn, but eventually boosts our ability to generalize the knowledge we learned to new contexts. This is because variability helps to highlight which features of the category are actually relevant, and which are not.
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Limor Raviv
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