Fully Convolutional Networks (FCNs) are being used for semantic segmentation of natural images, for multi-modal medical image analysis and multispectral satellite image segmentation. Very similar to deep classification networks like VGG, ResNet, AlexNet etc there is also a large variety of deep architectures that perform semantic segmentation. I summarize several networks like FCN, SegNet, U-Net, RefineNet, PSPNet, G-FRNet etc here and provide reference Keras and PyTorch implementations for a number of them.
3D-GANs are 3-dimensional fully convolutional part of the GAN family. Published at NIPS'16, this architecture with it's adversarial critereon can generate near-perfect 3D Volumes and the discriminator features can be also used in 3D Volume classification to get state-of-the-art scores on the ShapeNet benchmark.
If you happen to be working on huge datasets for Big Data or Machine Learning where the genome or image database is huge ( > 4 GB) and the data is available on a website only after user authentication, there are a lot of options to ponder over.
Was bored of editing every single `.html` file in my static site homepage and was very very difficult to manage the changes even with git. I had heard people saying cool stuff about Jekyll and its great static site generating power. And now that I have rolled this site up, I must say its magic :).
Instruction Set to install and configure Quartus and Modelsim for Krypton Boards on 64 bit Linux systemss
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