@Article{abderrahmane:tii:2020,
  author    = {Abderrahmane, Zineb and Ganesh, Gowrishankar and Crosnier, Andr{\'e} and Cherubini, Andrea},
  title     = {A Deep Learning Framework for Tactile Recognition of Known as Well as Novel Objects.},
  journal   = {IEEE Transactions on Industrial Informatics},
  year      = {2020},
  volume    = {16},
  number    = {1},
  pages     = {123--432},
  month     = {January},
  doi       = {10.1109/TII.2019.2898264},
  url       = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=8637832},
  keywords  = {Convolutional neural networks, deep learning, generative adversarial networks (GANs), one-shot learning (OSL), tactile object recognition, zero-shot learning (ZSL)},
  abstract  = {This paper addresses the recognition of daily-life objects by a robot equipped with tactile sensors. The main contribution is a deep learning framework that can recognize objects already touched as well as objects never touched before. To this end, we train a deconvolutional neural network that generates synthetic tactile data for novel classes. Then, we use both these synthetic data and the real data collected by touching objects, to train a convolutional neural network to recognize both known (trained) objects and novel objects. Furthermore, we propose a method for integrating newly encountered data into novel classes. Finally, we evaluate the framework using the largest available dataset of tactile objects descriptions.},
  publisher = {IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC},
  address   = {445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA}
}