title: Feature Transformers: A Unified Representation Learning Framework for Lifelong Learning
abstract: Despite the recent advances in representation learning, lifelong learning continues
  to be one of the most challenging and unconquered problems. Catastrophic forgetting
  and data privacy constitute two of the important challenges for a successful
  lifelong learner. Further, existing techniques are designed to handle only specific
  manifestations of lifelong learning, whereas a practical lifelong learner is expected
  to switch and adapt seamlessly to different scenarios. In this paper, we present a
  single, unified mathematical framework for handling the myriad variants of lifelong
  learning, while alleviating these two challenges. We utilize an external memory
  to store only the features representing past data and learn richer and newer
  representations incrementally through transformation neural networks - feature
  transformers. We define, simulate and demonstrate exemplary performance on a
  realistic lifelong experimental setting using the MNIST rotations dataset, paving
  the way for practical lifelong learners. To illustrate the applicability of our method
  in data sensitive domains like healthcare, we study the pneumothorax classification
  problem from X-ray images, achieving near gold standard performance.
  We also benchmark our approach with a number of state-of-the art methods on
  MNIST rotations and iCIFAR100 datasets demonstrating superior performance.
authors: Hariharan Ravishankar Rahul Venkataramani Saihareesh Anamandra Prasad Sudhakar
hIndex: Hariharan Ravishankar 7