Scattering Networks for Scene Labeling
Scattering networks [1,2] extract characteristic features from signals by recursively applying the composition of the following three operations: convolution with a set of ﬁlter functions, a non-linearity, and a sub-sampling step. Such networks, combined with a classiﬁer (such as, e.g., support vector machines) were successfully employed in a number of classiﬁcation tasks [1,3]. In contrast to traditional convolutional neural networks which learn the ﬁlters from training data , scattering networks use pre-deﬁned ﬁlters such as wavelets , curvelets , or shearlets . While a scattering network-based classiﬁer arguably leads to less ﬂexible machine learning models than convolutional network-based classiﬁers which learn the ﬁlters, they potentially allow for faster implementations thanks to the structure of the pre-deﬁned ﬁlters (e.g., tensorized wavelet filters).
This project shall explore the application of scattering network-based classifiers to the problem of scene labeling, whose aim is to assign a class label such as "street", "tree", or "building" to every pixel of an image. Applications of scene labeling include situational awareness systems , which often demand low-complexity and low-power scene labeling.
The goal of this project is to develop a scattering network-based classiﬁer for scene labeling and to realize a GPU implementation thereof (followed by VLSI/FPGA, if time permits).
- Fall Semester 2015
- Michael Tschannen (michaelt ät nari.ee.ethz.ch)
- Thomas Wiatowski (withomas ät nari.ee.ethz.ch)
- Lukas Cavigelli
- Michael Lerjen (mlerjen ät nari.ee.ethz.ch)
This project is a collaboration between the communication theory group and the digital circuits and systems lab.
- Matlab, C programming, linear algebra
- 0%-20% Theory
- 80%-100% Programming
- J. Bruna and S. Mallat, “Invariant scattering convolution networks,” IEEE Trans. Pattern Anal. Mach.Intell., vol. 35, no. 8, pp. 1872-1886, 2013. link
- T. Wiatowski and H. B¨olcskei, “Deep convolutional neural networks based on semi-discrete frames,” Proc. of IEEE International Conference on Information Theory (ISIT), pp. 1212-1216, 2015. link
- J. Andén and S. Mallat, “Deep scattering spectrum,” IEEE Trans. on Signal Process., vol. 62, no. 16, pp. 4114-4128, 2014. link
- Y. LeCun, L. Bottou, Y. Bengio, and P. Haﬀner, “Gradient-based learning applied to document recognition,” Proc. of the IEEE, pp. 2278-2324, 1998. link
- L. Cavigelli, M. Magno, and L. Benini, “Accelerating real-time embedded scene labeling with convolutional networks,” Proc. of ACM/EDAC/IEEE Design Automation Conference (DAC), pp. 1-6, 2015 link