2022/10/14 by Renán A. Rojas-Gómez, Renan A. Rojas-Gomez, Rojas-Gomez, Renan A. +8 · 8 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Human Pose and Action Recognition #Machine Learning (cs.LG) #Multimodal Machine Learning Applications #cs.AI #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2210.08001
Accepted at the Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022)
arxiv created 2022/10/14 · openalex publication_date 2022/10/14 · arxiv updated 2022/10/17 · openalex created_date 2022/10/20 · openalex updated_date 2026/07/28
We propose learnable polyphase sampling (LPS), a pair of learnable down/upsampling layers that enable truly shift-invariant and equivariant convolutional networks. LPS can be trained end-to-end from data and generalizes existing handcrafted downsampling layers. It is widely applicable as it can be integrated into any convolutional network by replacing down/upsampling layers. We evaluate LPS on image classification and semantic segmentation. Experiments show that LPS is on-par with or outperforms existing methods in both performance and shift consistency. For the first time, we achieve true shift-equivariance on semantic segmentation (PASCAL VOC), i.e., 100% shift consistency, outperforming baselines by an absolute 3.3%.