Findings from published research, checked in the open

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1,460 claims from 908 papers are on the record. 46 have been checked so far; the other 1,414 have no check with a result yet.

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Status: Unchecked ×Keyword: network depth ×Clear all

4 claims from 2 papers

1. Computer Science › Advanced Neural Network Applications

   Very Deep Convolutional Networks for Large-Scale Image Recognition

   Simonyan and Zisserman · arXiv (Cornell University) · 2014

   The paper tests how network depth affects accuracy in large-scale image recognition, finding that very small 3x3 filters with 16-19 weight layers improve markedly on earlier configurations.

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   1. UncheckedThe paper states that image representations learned by its very deep networks generalise well to other datasets, reaching state-of-the-art results there.“We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results.”
   2. UncheckedThe authors say their deeper-network findings underpinned their ImageNet 2014 entry, which placed first in localisation and second in classification.“These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively.”

2. Computer Science › Neural Networks and Applications

   Do Deep Nets Really Need to be Deep?

   Ba and Caruana · arXiv (Cornell University) · 2013

   The extended abstract reports that shallow networks trained to mimic deep models can match their accuracy on TIMIT phoneme recognition, sometimes with similar parameter counts.

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   1. UncheckedShallow feed-forward networks can learn functions previously learned by deep nets and reach accuracies once thought to need deep models.“In this extended abstract, we show that shallow feed-forward networks can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models.”
   2. UncheckedIn some cases, shallow neural networks can learn the functions of deep models using a similar total number of parameters to the original deep model.“Moreover, in some cases the shallow neural nets can learn these deep functions using a total number of parameters similar to the original deep model.”