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\(N^4\)-Fields: Neural Network Nearest Neighbor Fields for Image Transforms

  • Yaroslav GaninEmail author
  • Victor Lempitsky
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9004)

Abstract

We propose a new architecture for difficult image processing operations, such as natural edge detection or thin object segmentation. The architecture is based on a simple combination of convolutional neural networks with the nearest neighbor search.

We focus our attention on the situations when the desired image transformation is too hard for a neural network to learn explicitly. We show that in such situations the use of the nearest neighbor search on top of the network output allows to improve the results considerably and to account for the underfitting effect during the neural network training. The approach is validated on three challenging benchmarks, where the performance of the proposed architecture matches or exceeds the state-of-the-art.

Keywords

Training Image Neighbor Search Image Patch Convolutional Neural Network Neural Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Skolkovo Institute of Science and Technology (Skoltech)MoscowRussia

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