Downsampling Layer

Downsampling layers in neural networks reduce the spatial dimensions of feature maps, impacting computational efficiency and the scale of feature analysis. Current research focuses on developing more sophisticated downsampling methods, including learnable approaches that optimize strides and sampling strategies within convolutional and transformer architectures, as well as those tailored for specific data types like point clouds and spiking neural networks. These advancements aim to improve model performance, interpretability (e.g., reducing noise in saliency maps), and efficiency by addressing information loss and the limitations of traditional methods like fixed-stride convolutions and pooling. The resulting improvements have significant implications for various computer vision tasks, including image classification, inpainting, and semantic segmentation.