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Progressive Growing of GANs for Improved Quality, Stability, and Variation
We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add newExpand
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A Style-Based Generator Architecture for Generative Adversarial Networks
We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervisedExpand
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Temporal Ensembling for Semi-Supervised Learning
In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled. We introduceExpand
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Pruning Convolutional Neural Networks for Resource Efficient Inference
We propose a new formulation for pruning convolutional kernels in neural networks to enable efficient inference. We interleave greedy criteria-based pruning with fine-tuning by backpropagation - aExpand
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Noise2Noise: Learning Image Restoration without Clean Data
We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: under certainExpand
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Analyzing and Improving the Image Quality of StyleGAN
The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, andExpand
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Understanding the efficiency of ray traversal on GPUs
We discuss the mapping of elementary ray tracing operations---acceleration structure traversal and primitive intersection---onto wide SIMD/SIMT machines. Our focus is on NVIDIA GPUs, but some of theExpand
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Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning
We propose a new framework for pruning convolutional kernels in neural networks to enable efficient inference, focusing on transfer learning where large and potentially unwieldy pretrained networksExpand
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Few-Shot Unsupervised Image-to-Image Translation
Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images. WhileExpand
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Fast parallel construction of high-quality bounding volume hierarchies
We propose a new massively parallel algorithm for constructing high-quality bounding volume hierarchies (BVHs) for ray tracing. The algorithm is based on modifying an existing BVH to improve itsExpand
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