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Boosted Training of Convolutional Neural Networks for Multi-Class Segmentation
Training deep neural networks on large and sparse datasets is still challenging and can require large amounts of computation and memory.
An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation
Deep convolutional neural networks (CNNs) have shown excellent performance in object recognition tasks and dense classification problems such as semantic segmentation.
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