Web: http://arxiv.org/abs/2201.11103

Jan. 27, 2022, 2:10 a.m. | Konstantin Ditschuneit, Johannes S. Otterbach

stat.ML updates on arXiv.org arxiv.org

State-of-the-art semantic segmentation models are characterized by high
parameter counts and slow inference times, making them unsuitable for
deployment in resource-constrained environments. To address this challenge, we
propose \textsc{Auto-Compressing Subset Pruning}, \acosp, as a new online
compression method. The core of \acosp consists of learning a channel selection
mechanism for individual channels of each convolution in the segmentation model
based on an effective temperature annealing schedule. We show a crucial
interplay between providing a high-capacity model at the beginning of …

arxiv cv segmentation semantic

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