Non-local Neural Network

This parity illustrates the algorithm's robustness since, in expectation, the Euclidean distance conserves the sequence of resemblance among pixels. The NL-means, in addition to giving the comparison of the grey level in a single point, can compare the geometrical configuration in an entire neighborhood. A team in Carnegie Mellon University and Facebook AI Research, has been inspired by the above-mentioned classical non-local means and developed a non-local operation. It is usual to utilize CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) architectures in long-range dependency modeling for sequential data (image, time-series, signal, …), and these both process a local neighborhood. Consequently, we have to apply these local operations frequently. This repeating has some limitations; being computationally inefficient and making optimization harder.