# Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks

## TL;DR

This paper introduces displaced aggregation units (DAUs), a novel convolution filter that learns spatial displacements to adapt receptive fields, enabling more compact and efficient neural networks across various vision tasks.

## Contribution

The paper proposes DAUs, a new convolutional filter that automatically adapts receptive fields, replacing hand-crafted modifications and improving network compactness and efficiency.

## Key findings

- Up to four times more compact networks achieved
- Comparable or better performance on multiple tasks
- Effective adaptation of receptive fields without manual tuning

## Abstract

Convolutional neural networks excel in a number of computer vision tasks. One of their most crucial architectural elements is the effective receptive field size, that has to be manually set to accommodate a specific task. Standard solutions involve large kernels, down/up-sampling and dilated convolutions. These require testing a variety of dilation and down/up-sampling factors and result in non-compact representations and excessive number of parameters. We address this issue by proposing a new convolution filter composed of displaced aggregation units (DAU). DAUs learn spatial displacements and adapt the receptive field sizes of individual convolution filters to a given problem, thus eliminating the need for hand-crafted modifications. DAUs provide a seamless substitution of convolutional filters in existing state-of-the-art architectures, which we demonstrate on AlexNet, ResNet50, ResNet101, DeepLab and SRN-DeblurNet. The benefits of this design are demonstrated on a variety of computer vision tasks and datasets, such as image classification (ILSVRC 2012), semantic segmentation (PASCAL VOC 2011, Cityscape) and blind image de-blurring (GOPRO). Results show that DAUs efficiently allocate parameters resulting in up to four times more compact networks at similar or better performance.