Residual networks reformulate layers to learn residual functions, enabling effective training of up to 152-layer models that achieve 3.57% error on ImageNet and win ILSVRC 2015.
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An iterative multi-task GAN-based framework completes occluded vehicle segmentation masks and recovers invisible appearance using coupled discriminators, a 3D silhouette pool, and a shared two-path network, outperforming prior methods on a new synthetic-plus-real dataset.
DeepLabv3 improves semantic segmentation by capturing multi-scale context with cascaded or parallel atrous convolutions and adding global context to ASPP, achieving better results on PASCAL VOC 2012 without DenseCRF post-processing.
Linear learning-rate scaling plus warmup lets minibatch size 8192 train ResNet-50 on ImageNet in one hour at full small-batch accuracy.
citing papers explorer
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Deep Residual Learning for Image Recognition
Residual networks reformulate layers to learn residual functions, enabling effective training of up to 152-layer models that achieve 3.57% error on ImageNet and win ILSVRC 2015.
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Visualizing the Invisible: Occluded Vehicle Segmentation and Recovery
An iterative multi-task GAN-based framework completes occluded vehicle segmentation masks and recovers invisible appearance using coupled discriminators, a 3D silhouette pool, and a shared two-path network, outperforming prior methods on a new synthetic-plus-real dataset.
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Rethinking Atrous Convolution for Semantic Image Segmentation
DeepLabv3 improves semantic segmentation by capturing multi-scale context with cascaded or parallel atrous convolutions and adding global context to ASPP, achieving better results on PASCAL VOC 2012 without DenseCRF post-processing.
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Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Linear learning-rate scaling plus warmup lets minibatch size 8192 train ResNet-50 on ImageNet in one hour at full small-batch accuracy.