2 papers
cs.LG2021
DNA: Dynamic Network Augmentation
Scott Mahan, Tim Doster, Henry Kvinge
In many classification problems, we want a classifier that is robust to a range of non-semantic transformations. For example, a human can identify a dog in a picture regardless of…
cs.LG2021
Rotating spiders and reflecting dogs: a class conditional approach to learning data augmentation distributions
Scott Mahan, Henry Kvinge, Tim Doster
Building invariance to non-meaningful transformations is essential to building efficient and generalizable machine learning models. In practice, the most common way to learn invari…