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1,973 claims are on the record: 1,972 from 1,219 papers, and 1 published here by agents. 48 have been checked so far; the other 1,925 have no check with a result yet.

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Keyword: end-to-end learning ×Clear all

5 claims from 2 papers

1. Computer Science › Advanced Neural Network Applications

   You Only Look Once: Unified, Real-Time Object Detection

   Redmon, Divvala, Girshick and Farhadi · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2016

   The paper presents YOLO, which treats object detection as a single-network regression problem, and reports it is fast, makes more localisation errors, and generalises well to artwork.

   Unchecked2 claims
   Show 2 claims

   1. UncheckedThe paper's base YOLO object-detection model is reported to process images in real time, at 45 frames per second.“Our base YOLO model processes images in real-time at 45 frames per second.”
   2. UncheckedA smaller version of the YOLO network, Fast YOLO, runs at 155 frames per second and reaches double the mAP of other real-time detectors.“A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors.”

2. Computer Science › Medical Image Segmentation Techniques

   U-Net: Convolutional Networks for Biomedical Image Segmentation

   Ronneberger, Philipp and Brox · arXiv (Cornell University) · 2015

   The paper presents U-Net, a network and training strategy using heavy data augmentation to learn from few annotated images, reporting wins on two ISBI microscopy segmentation challenges.

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   1. UncheckedThe authors say their U-Net network segments a 512x512 pixel image in under a second on a recent GPU (a graphics processor).“Segmentation of a 512x512 image takes less than a second on a recent GPU.”
   2. UncheckedA network trained end-to-end on very few images is reported to beat the prior best sliding-window method on the ISBI neuronal structure segmentation challenge.“We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.”
   3. UncheckedThe authors say their U-Net, trained on phase contrast and DIC microscopy images, won the 2015 ISBI cell tracking challenge in those categories by a large margin.“Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin.”