- Abstract: This paper shows how, by combining prior and supervised representations, one can create architectures that lead to nearly state-of-the-art results on standard benchmarks, which mean they perform as well as a deep network learned from scratch. We use scattering as a generic and fixed initialization of the first layers of a deep network, and learn the remaining layers in a supervised manner. We numerically demonstrate that deep hybrid scattering networks generalize better on small datasets than supervised deep networks. Scattering networks could help current systems to save computation time, while guaranteeing the stability to geometric transformations and noise of the first internal layers. We also show that the learned operators explicitly build invariances to geometrical variabilities, such as local rotation and translation, by analyzing the third layer of our architecture. We demonstrate that it is possible to replace the scattering transform by a standard deep network at the cost of having to learn more parameters and potentially adding instabilities. Finally, we release a new software, ScatWave, using GPUs for fast computations of a scattering network that is integrated in Torch. We evaluate our model on the CIFAR10, CIFAR100 and STL10 datasets.
- TL;DR: This paper shows how, by combining prior and supervised representations, one can create architectures that lead to nearly state-of-the-art results on standard benchmarks.
- Conflicts: ens.fr
- Keywords: Computer vision, Unsupervised Learning, Deep learning