Computer Science > Computer Vision and Pattern Recognition
arXiv:2210.08951 (cs)
[Submitted on 17 Oct 2022]
Title:Approximating Continuous Convolutions for Deep Network Compression
View a PDF of the paper titled Approximating Continuous Convolutions for Deep Network Compression, by Theo W. Costain and 1 other authors
View PDFAbstract:We present ApproxConv, a novel method for compressing the layers of a convolutional neural network. Reframing conventional discrete convolution as continuous convolution of parametrised functions over space, we use functional approximations to capture the essential structures of CNN filters with fewer parameters than conventional operations. Our method is able to reduce the size of trained CNN layers requiring only a small amount of fine-tuning. We show that our method is able to compress existing deep network models by half whilst losing only 1.86% accuracy. Further, we demonstrate that our method is compatible with other compression methods like quantisation allowing for further reductions in model size.
| Comments: | BMVC 2022 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2210.08951 [cs.CV] |
| (orarXiv:2210.08951v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2210.08951 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Approximating Continuous Convolutions for Deep Network Compression, by Theo W. Costain and 1 other authors
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