The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Matrix Backpropagation for Deep Networks with Structured Layers
Abstract
Deep neural network architectures have recently produced excellent results in a variety of areas in artificial intelligence and visual recognition, well surpassing traditional shallow architectures trained using hand-designed features. The power of deep networks stems both from their ability to perform local computations followed by pointwise non-linearities over increasingly larger receptive fields, and from the simplicity and scalability of the gradient-descent training procedure based on backpropagation. An open problem is the inclusion of layers that perform global, structured matrix computations like segmentation (e.g. normalized cuts) or higher-order pooling (e.g. log-tangent space metrics defined over the manifold of symmetric positive definite matrices) while preserving the validity and efficiency of an end-to-end deep training framework. In this paper we propose a sound mathematical apparatus to formally integrate global structured computation into deep computation architectures. At the heart of our methodology is the development of the theory and practice of backpropagation that generalizes to the calculus of adjoint matrix variations. We perform segmentation experiments using the BSDS and MSCOCO benchmarks and demonstrate that deep networks relying on second-order pooling and normalized cuts layers, trained end-to-end using matrix backpropagation, outperform counterparts that do not take advantage of such global layers.
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Global Second-Order Pooling Convolutional Networks
Gao, Z., Xie, J., Wang, Q., & Li, P. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00314 · Cited by 524
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Lin, T.-Y., RoyChowdhury, A., & Maji, S. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2723400 · Cited by 328
End-to-End Representation Learning for Correlation Filter Based Tracking
Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., & Torr, P. H. S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.531 · Cited by 274
Image-To-Image Translation via Group-Wise Deep Whitening-And-Coloring Transformation
Cho, W., Choi, S., Park, D. K., Shin, I., & Choo, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01089 · Cited by 154
“Furthermore, computing the backpropagation with respect to singular value decomposition involved in WCT is nontrivial [30, 15].”
Method
Metadata
- Authors
- Ionescu, C., Vantzos, O., & Sminchisescu, C.
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 2965-2973
- OpenAlex
- W2204257188
- Cited by
- 281
- References
- 9
- FWCI
- 9.70
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Ionescu, C., Vantzos, O., & Sminchisescu, C. (2015). Matrix Backpropagation for Deep Networks with Structured Layers. 2015 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2015.339
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