Training Deep Neural Networks on Noisy Labels with Bootstrapping
Reed, Scott E. and Lee, Honglak and Anguelov, Dragomir and Szegedy, Christian and Erhan, Dumitru and Rabinovich, Andrew
arXiv e-Print archive - 2014 via Local Bibsonomy
Keywords: dblp
Reed, Scott E. and Lee, Honglak and Anguelov, Dragomir and Szegedy, Christian and Erhan, Dumitru and Rabinovich, Andrew
arXiv e-Print archive - 2014 via Local Bibsonomy
Keywords: dblp
[link]
_Objective:_ Design a loss to make deep network robust to label noise. _Dataset:_ [MNIST](yann.lecun.com/exdb/mnist/), Toroto Faces Database, [ILSVRC2014](http://www.image-net.org/challenges/LSVRC/2014/). #### Inner-workings: Three types of losses are presented: * reconstruciton loss: [](https://user-images.githubusercontent.com/17261080/27532200-bb42b8a6-5a5f-11e7-8c14-673958216bfc.png) * soft bootstrapping which uses the predicted labels by the network `qk` and the user-provided labels `tk`: [](https://user-images.githubusercontent.com/17261080/27532296-1e01a420-5a60-11e7-9273-d1affb0d7c2e.png) * hard bootstrapping replaces the soft predicted labels by their binary version: [](https://user-images.githubusercontent.com/17261080/27532439-a3f9dbd8-5a60-11e7-91a7-327efc748eae.png) [](https://user-images.githubusercontent.com/17261080/27532463-b52f4ab4-5a60-11e7-9aed-615109b61bd8.png) #### Architecture: They test with Feed Forward Neural Networks only. #### Results: They use only permutation noise with a very high probability compared with what we might encounter in real-life. [](https://user-images.githubusercontent.com/17261080/27533105-b051d366-5a62-11e7-95f3-168d0d2d7841.png) The improvement for small noise probability (<10%) might not be that interesting. 
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