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Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks Renan A. Rojas-Gomez T eck-Yian Lim Alexander G. Schwing Minh N. Do Raymond A. Y eh
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 (P ASCAL VOC), i.e., 100% shift consistency, outperforming baselines by an absolute 3.3%.
Human head transplants' gory, Frankenstein-esque history
Breakthroughs, discoveries, and DIY tips sent six days a week. In Mary Shelley's, a mad scientist creates a monstrous creature with severed body parts. In certain film adaptations, a dismembered head is tacked onto the malformed body. Then, with the help of a lightning storm, a new life is born. From the first successful kidney transplant in 1954, modern organ transplantation has often been linked to the horrors of Frankenstein .