Atrous Space Bender U-Net (ASBU-Net/LogiNet)
Anurag Bansal, Oleg Ostap, Miguel Maestre Trueba, Kristopher Perry

TL;DR
ASBU-Net is a fast, efficient CNN for high-resolution image segmentation that uses a novel atrous space bender layer, enabling easy deployment on hardware with limited memory while maintaining high accuracy.
Contribution
The paper introduces ASBU-Net with a new atrous space bender layer, improving efficiency and hardware compatibility for semantic segmentation tasks.
Findings
Strong performance on resource and accuracy trade-offs
Compatible with FPGA and embedded hardware
No special layers needed for quantization
Abstract
With recent advances in CNNs, exceptional improvements have been made in semantic segmentation of high resolution images in terms of accuracy and latency. However, challenges still remain in detecting objects in crowded scenes, large scale variations, partial occlusion, and distortions, while still maintaining mobility and latency. We introduce a fast and efficient convolutional neural network, ASBU-Net, for semantic segmentation of high resolution images that addresses these problems and uses no novelty layers for ease of quantization and embedded hardware support. ASBU-Net is based on a new feature extraction module, atrous space bender layer (ASBL), which is efficient in terms of computation and memory. The ASB layers form a building block that is used to make ASBNet. Since this network does not use any special layers it can be easily implemented, quantized and deployed on FPGAs…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Neural Network Applications · Brain Tumor Detection and Classification · COVID-19 diagnosis using AI
