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Status: Unchecked ×Keyword: cardinality ×Clear all

3 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Aggregated Residual Transformations for Deep Neural Networks

   Xie, Girshick, Dollár, Tu and He · arXiv (Cornell University) · 2016

   The paper presents ResNeXt, a modular image-classification network built from repeated blocks of parallel transformations, and reports that increasing cardinality helps accuracy on ImageNet-1K and other tasks.

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   1. UncheckedOn ImageNet-1K, raising 'cardinality' (number of parallel branches) improved image classification accuracy even when model complexity was held constant.“On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy.”
   2. UncheckedWhen a network's capacity is increased, adding more parallel branches (cardinality) is reported to help more than adding layers or widening them.“Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity.”
   3. UncheckedThe authors report that ResNeXt also outperforms its ResNet counterpart on an ImageNet-5K set and on the COCO object detection set.“We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart.”