Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation

9 Oct 2015Søren HaubergOren FreifeldAnders Boesen Lindbo LarsenJohn W. Fisher IIILars Kai Hansen

Data augmentation is a key element in training high-dimensional models. In this approach, one synthesizes new observations by applying pre-specified transformations to the original training data; e.g.~new images are formed by rotating old ones... (read more)

PDF Abstract


No code implementations yet. Submit your code now

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet