A PyTorch Implementation code for developing super fast adversarial training

This is a PyTorch Implementation code for developing super fast adversarial training. This code is combined with below state-of-the-art technologies for accelerating adversarial attacks and defenses with Deep Neural Networks on Volta GPU architecture. Distributed Data Parallel [link] Channel Last Memory Format [link] Mixed Precision Training [link] Mixed Precision + Adversarial Attack (based on torchattacks [link]) Faster Adversarial Training for Large Dataset [link] Fast    

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A PyTorch implementation of Neural Radiance Fields (NeRF) for reproduction of results whilst running at a faster speed

NeRF (Neural Radiance Fields) is a method for achieving outcomes for synthesizing novel views of complex scenes. Posted below are a few videos generated by this project. This project is a PyTorch implementation of NeRF that reproduces the results while running approximately 1.3x faster. The code is based on the TensorFlow implementation here of the author. PyTorch has been used for numerical testing and validation of the same. Installation git clone https://github.com/yenchenlin/nerf-pytorch.git cd nerf-pytorch pip install -r requirements.txt cd torchsearchsorted […]

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Pytorch implementation of MaskGIT: Masked Generative Image Transformer

Pytorch implementation of MaskGIT: Masked Generative Image Transformer (https://arxiv.org/pdf/2202.04200.pdf) Note: this is work in progress MaskGIT is an extension to the VQGAN paper which improves the second stage transformer part (and leaves the first stage untouched). It switches the unidirectional transformer for a bidirectional transformer. The (second stage) training is pretty similar to BERT by randomly masking out tokens and trying to predict these using the bidirectional transformer (the original work used a GPT architecture randomly replaced tokens by other […]

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Sharpened Cosine Distance implementation in PyTorch

PyTorch implementation of the Sharpened Cosine Distance operator. The core idea came from Brandon Rohrer(@brohrer) and the implementationis based on the tf/keras implementation of Raphael Pisoni. This implementation supports 2D operation only asymmetric kernels, any shape CUDA / GPU If you find this implementation useful please give it a star. Open issues for bugs/ideas. If you are planning to build something on top of it let me know, I am always up for some good collaborations ๐Ÿ˜‰ GitHub View Github […]

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Pytorch ViT for Image classification on the CIFAR10 dataset

Introduction This project uses ViT to perform image classification tasks on DATA set CIFAR10. The implement of Vit and pretrained weight are from https://github.com/asyml/vision-transformer-pytorch. Installation Create environment: conda create –name vit –file requirements.txt conda activate vit Datasets Download the CIFAR10 from http://www.cs.toronto.edu/~kriz/cifar.html, creat data floder and unzip the cifar-10-python.tar.gz in โ€˜data/โ€™. python main.py GitHub View Github    

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A PyTorch implementation for Transition Matrix Representation of Trees with Transposed Convolutions

This project is a PyTorch implementation for Transition Matrix Representation of Trees with Transposed Convolutions, published as a conference proceeding atSDM 2022. The paper proposes TART (Transition Matrix Representation withTransposed Convolutions), a novel framework for generalizing tree models with aunifying view. Requirements The repository is written by Python 3.7 with the packages listed inrequirements.txt. A GPU environment is strongly recommended for efficienttraining and inference of our model. You can type the following command toinstall the required packages: pip install -r […]

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