2022/01/26 by Konstantin Ditschuneit, Ditschuneit, Konstantin, Johannes Otterbach +1 · 1 citation
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (stat.ML) #Multimodal Machine Learning Applications
paper · pdf · doi:10.48550/arxiv.2201.11103
openalex publication_date 2022/01/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 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 training and the compression pressure forcing the model to compress concepts into retained channels. We apply \acosp to \segnet and \pspnet architectures and show its success when trained on the \camvid, \city, \voc, and \ade datasets. The results are competitive with existing baselines for compression of segmentation models at low compression ratios and outperform them significantly at high compression ratios, yielding acceptable results even when removing more than 93% of the parameters. In addition, \acosp is conceptually simple, easy to implement, and can readily be generalized to other data modalities, tasks, and architectures. Our code is available at \urlhttps://github.com/merantix/acosp.