Select, Attend, and Transfer: Light, Learnable Skip Connections

TL;DR

This paper introduces light, learnable skip connections for segmentation networks that select discriminative features and regions, reducing memory and parameters while improving accuracy across multiple datasets.

Contribution

The paper proposes a novel learnable skip connection method that enhances segmentation performance and efficiency by focusing on discriminative features and regions.

Findings

Outperforms traditional skip connections in accuracy

Reduces memory and parameter usage

Effective on 2D and volumetric datasets

Abstract

Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risks for vanishing gradients. They equip encoder-decoder-like networks with richer feature representations, but at the cost of higher memory usage, computation, and possibly resulting in transferring non-discriminative feature maps. In this paper, we focus on improving skip connections used in segmentation networks (e.g., U-Net, V-Net, and The One Hundred Layers Tiramisu (DensNet) architectures). We propose light, learnable skip connections which learn to first select the most discriminative channels and then attend to the most discriminative regions of the selected feature maps. The output of the proposed skip connections is a unique feature map which not only reduces the memory usage and network parameters to a high extent, but also improves segmentation accuracy. We evaluate the proposed method on three different 2D and volumetric datasets and demonstrate that the proposed light, learnable skip connections can outperform the traditional heavy skip connections in terms of segmentation accuracy, memory usage, and number of network parameters.