@article{1446313,
      author = {Betti, Alessandro, and Gori, Marco, and Melacci, Stefano,},
      url = {http://library.usi.edu/record/1446313},
      title = {Deep learning to see : towards new foundations of computer  vision /},
      abstract = {The remarkable progress in computer vision over the last  few years is, by and large, attributed to deep learning,  fueled by the availability of huge sets of labeled data,  and paired with the explosive growth of the GPU paradigm.  While subscribing to this view, this book criticizes the  supposed scientific progress in the field, and proposes the  investigation of vision within the framework of  information-based laws of nature. Specifically, the present  work poses fundamental questions about vision that remain  far from understood, leading the reader on a journey  populated by novel challenges resonating with the  foundations of machine learning. The central thesis is that  for a deeper understanding of visual computational  processes, it is necessary to look beyond the applications  of general purpose machine learning algorithms, and focus  instead on appropriate learning theories that take into  account the spatiotemporal nature of the visual signal.  Topics and features: Presents a curiosity-driven approach,  posing questions to stimulate readers to design novel  computational models of vision Offers a rethinking of  computer vision, arguing for an approach based on vision in  nature, versus regarding visual signals as collections of  images Provides an interdisciplinary commentary, aiming to  unify computer vision, machine learning, human vision, and  computational neuroscience Serving to inspire and stimulate  critical reflection and discussion, yet requiring no prior  advanced technical knowledge, the text can naturally be  paired with classic textbooks on computer vision to better  frame the current state of the art, open problems, and  novel potential solutions. This unique volume will be of  great benefit to graduate and advanced undergraduate  students in computer science, computational neuroscience,  physics, and other related disciplines.},
      doi = {https://doi.org/10.1007/978-3-030-90987-1},
      recid = {1446313},
      pages = {1 online resource :},
}