The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
Merges the ideas of the U-Net and the DenseNet, this new neural network is especially good for huge datasets in image segmentation.
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U-Net: Convolutional Networks for Biomedical Image Segmentation arxiv.org
The U-Net is an encoder-decoder CNN that also has skip-connections, good for image segmentation at a per-pixel level.
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Densely Connected Convolutional Networks arxiv.org
Best Paper Award at CVPR 2017, yielding improvements on state-of-the-art performances on CIFAR-10, CIFAR-100 and SVHN datasets, this new neural network architecture is named DenseNet.
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Very Deep Convolutional Networks for Large-Scale Image Recognition arxiv.org
Interesting idea of stacking multiple 3x3 conv+ReLU before pooling for a bigger filter size with just a few parameters. There is also a nice table for "ConvNet Configuration".
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Going Deeper with Convolutions cv-foundation.org
GoogLeNet: Appearance of "Inception" layers/modules, the idea is of parallelizing conv layers into many mini-conv of different size with "same" padding, concatenated on depth.
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TF-Unet github.com
General purpose U-Network implemented in Keras for image segmentation
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Deep Learning videolectures.net
Education › Papers > Convolutional Neural Networks: “Merges the ideas of the U-Net and the DenseNet, this new neural network is especially good for huge datasets in image segmentation.”