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Locally Adaptive Learning Loss for Semantic Image Segmentation
[article]
2018
arXiv
pre-print
We propose a novel locally adaptive learning estimator for enhancing the inter- and intra- discriminative capabilities of Deep Neural Networks, which can be used as improved loss layer for semantic image segmentation tasks. Most loss layers compute pixel-wise cost between feature maps and ground truths, ignoring spatial layouts and interactions between neighboring pixels with same object category, and thus networks cannot be effectively sensitive to intra-class connections. Stride by stride,
arXiv:1802.08290v2
fatcat:aou5jrcjqjfdhk5n4q6fpsu5t4