List of relevant resources

[barisozmen]

Journal papers

• AutoAugment: Learning Augmentation Policies from Data (link)
• Smart Augmentation - Learning an Optimal Data Augmentation Strategy (link)
• Adaptive data augmentation for image classification (link)
• Learning to Compose Domain-Specific Transformations for Data Augmentation (link)
  • Cited by AutoAugment paper

AutoAugment implementations

• Official (github)
  • Tensorflow used
  • does not have controller (RL) part
• Unofficial (github)
  • Keras used
  • Have the controller part
• Unofficial exploration on jupyter notebook (github)
  • Very good for learning
  • Pytorch used
  • Doesn't implement the whole
• In tensorflow-hub (link)

  Blogs

• https://towardsdatascience.com/how-to-improve-your-image-classifier-with-googles-autoaugment-77643f0be0c9
• Comparing accuracy increase of various augmentation methods (https://medium.com/nanonets/how-to-use-deep-learning-when-you-have-limited-data-part-2-data-augmentation-c26971dc8ced)
  • NanoNets
• A bag of tricks for image classification (https://towardsdatascience.com/a-big-of-tricks-for-image-classification-fec41eb28e01)
  • Occlusion (putting grey rectangle to some areas of image) as a data augmentation method.
• Deep neuro-evolution (DL + GA) (https://eng.uber.com/deep-neuroevolution/) (github)
  • Ekin Dogus suggested at this video (12:17)
  • Uber AI team hybridizing deep learning with genetic algorithms

    Videos

• Seminar presentation (Youtube)
  • distills the situation and the model well!
  • discussion part gives a good critic, data augmentation might overfit for the validation set

Libraries

• Tanda Learning to Compose Domain-Specific Transformations for Data Augmentation
• Automold road augmentation library for self-driving cars
• Albumentations general data augmentation library
• Autosat Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds
• NAS
• autokeras

Ideas

• How to make AutoAugment scalable?
  • By training models with 50-100 epochs in the beginning with original data. And then training it with suggested augmentations!
    • Training times of various models and datasets (https://dawn.cs.stanford.edu/benchmark/)
    • The DAWNbench paper (https://dawn.cs.stanford.edu/benchmark/papers/nips17-dawnbench.pdf)
• Transfer learning (TL) for decreasing computation time
• Transfer learning and genetic algorithms
• A case for using Random Search instead of RL:
  • "Typically random search algorithms sacrifice a guarantee of optimality for finding a good solution quickly with convergence results in probability." (Random Search Algorithms)
  • Also see: "Simple random search provides a competitive approach to reinforcement learning" (https://arxiv.org/abs/1803.07055)
  • Data augmentation guided by cooccurrences of objects
  • (https://www.researchgate.net/publication/328899557_Learning_data_augmentation_policies_using_augmented_random_search) paper claims that they get better results than AutoAugment by tweaking its parameters