Abstract: We investigate multiple techniques to improve upon the current state of the art deep convolutional neural network based image classification pipeline. The techiques include adding more image transformations to training data, adding more transformations to generate additional predictions at test time and using complementary models applied to higher resolution images. This paper summarizes our entry in the Imagenet Large Scale Visual Recognition Challenge 2013. Our system achieved a top 5 classification error rate of 13.55% using no external data which is over a 20% relative improvement on the previous year's winner.
Some Improvements on Deep Convolutional Neural Network Based Image Classification
Published 2013 in International Conference on Learning Representations
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- Publication year
2013
- Venue
International Conference on Learning Representations
- Publication date
2013-12-18
- Fields of study
Computer Science
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