Switchable Normalization learns per-layer weights to combine channel, layer, and minibatch normalizers, claiming robustness to batch size and better results than fixed normalizers on ImageNet, COCO, CityScapes, ADE20K, MegaFace, and Kinetics.
Imagenet classification with deep convolutional neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2019 2verdicts
UNVERDICTED 2representative citing papers
Proposes RMFN, a CNN modification that aggregates multi-scale local features to highlight lesion regions for pancreatitis recognition on a new hospital CT database.
citing papers explorer
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Switchable Normalization for Learning-to-Normalize Deep Representation
Switchable Normalization learns per-layer weights to combine channel, layer, and minibatch normalizers, claiming robustness to batch size and better results than fixed normalizers on ImageNet, COCO, CityScapes, ADE20K, MegaFace, and Kinetics.
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Region-Manipulated Fusion Networks for Pancreatitis Recognition
Proposes RMFN, a CNN modification that aggregates multi-scale local features to highlight lesion regions for pancreatitis recognition on a new hospital CT database.