IAM

TAG»TORCH«

NOVEMBER2022

PROJECT

Torch/CUDA implementation of batch normalization for OctNets.

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AUGUST2022

PROJECT

Basic and advanced torch examples, template for implementing custom C/CUDA modules and implementations of variational auto-encoders.

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ARTICLE

More Examples for Working with Torch

This article is a short follow-up on my initial collection of examples for getting started with Torch. In the meanwhile, through a series of additional articles, the corresponding GitHub repository has grown, including not only basic examples but also more advanced examples such as variational auto-encoders, generative adversarial networks or adversarial auto encoders. This article aims to provide a short overview of the added examples.

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ARTICLE

Convolutional Batch Normalization for OctNets

During my master thesis I partly worked on OctNets, octree-bases convolutional neural networks for efficient learning in 3D. Among others, I implemented convolutional batch normalization for OctNets. This article briefly discusses the implementation, which will be available on GitHub.

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ARTICLE

Denoising Variational Auto-Encoder in Torch

Based on the Torch implementation of a vanilla variational auto-encoder in a previous article, this article discusses an implementation of a denoising variational auto-encoder. While the theory of denoising variational auto-encoders is more involved, an implementation merely requires a suitable noise model.

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ARTICLE

Bernoulli Variational Auto-Encoder in Torch

After formally introducing the concept of categorical variational auto-encoders in a previous article, this article presents a practical Torch implementation of variational auto-encoders with Bernoulli latent variables.

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ARTICLE

Variational Auto-Encoder in Torch

After introducing the mathematics of variational auto-encoders in a previous article, this article presents an implementation in LUA using Torch. The main challenge when implementing variational auto-encoders are the Kullback-Leibler divergence as well as the reparameterization sampler. Here, both are implemented as separate nn modules.

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ARTICLE

Implementing Torch Modules in C/CUDA

Torch is a framework for scientific computing in LUA. However, it has mostly been used for deep learning research as it provides efficient and comfortable C/CUDA implementations of a wide range of (convolutional and/or recurrent) neural network components. In this article, I want to provide a code template allowing to easily extend torch.nn by custom modules implemented in C and/or CUDA without knowledge of Torch’s core.

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ARTICLE

PointNet Auto-Encoder in Torch

Recently proposed neural network architectures, including PointNets and PointSetGeneration networks, allow deep learning on unordered point clouds. In this article, I present a Torch implementation of a PointNet auto-encoder — a network allowing to reconstruct point clouds through a lower-dimensional bottleneck. As loss during training, I implemented a symmetric Chamfer distance in C/CUDA and provide the code on GitHUb.

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ARTICLE

ArXiv Pre-Print Improved Weakly-Supervised 3D Shape Completion Code Released

We are releasing the code and data corresponding to our ArXiv pre-print on weakly-supervised 3D shape completion — a follow-up work on our earlier CVPR’18 paper. The article provides links to the GitHub repositories and data downloads as well as detailed descriptions. It also highlights the differences between the two papers.

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