mlsanityData to 7 Oct 2026

N4 -Fields: Neural Network Nearest Neighbor Fields for Image Transforms

arXiv 1406.6558 ↗First posted 25 Jun 2014cs.CVcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jun 2014.

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Citing papers per year: 2021: 3; 2022: 2; 2023: 0; 2024: 3; 2025: 6.

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