Abstract
Pooling layers (e.g., max and average) may overlook important information encoded in the spatial arrangement of pixel intensity and/or feature values. We propose a novel lacunarity pooling layer that aims to capture the spatial heterogeneity of the feature maps by evaluating the variability within local windows. The layer operates at multiple scales, allowing the network to adaptively learn hierarchical features. The lacunarity pooling layer can be seamlessly integrated into any artificial neural network architecture. Experimental results demonstrate the layer's effectiveness in capturing intricate spatial patterns, leading to improved feature extraction capabilities. The proposed approach holds promise in various domains, especially in agricultural image analysis tasks. This work contributes to the evolving landscape of artificial neural network architectures by introducing a novel pooling layer that enriches the representation of spatial features. Our code is publicly available.
Abstract (translated)
池化层（例如，最大和平均池化层）可能忽略了像素强度和/或特征值空间排列中编码的重要信息。我们提出了一种新型的局部化池化层，旨在通过评估局部窗口内的方差来捕捉特征图的空间异质性。该层在多个尺度上运行，允许网络自适应地学习层次特征。局部化池化层可以轻松地集成到任何人工神经网络架构中。实验结果表明，该层有效地捕捉了复杂的空间模式，从而提高了特征提取能力。与农业图像分析任务相关的各种领域都具有重要意义。通过引入一种新颖的池化层，丰富了空间特征的表示，为人工神经网络架构的发展做出了贡献。我们的代码是公开可用的。
URL
https://arxiv.org/abs/2404.16268
