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Check out our latest research on adversarial robustness and generalization of deep networks.

TAG»LUA«

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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19thMAY2018

PROJECT

Learning 3D shape completion under weak supervision; on ShapeNet, ModelNet, KITTI and Kinect data; published at CVPR and on ArXiv.

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ARTICLE

CVPR’18 Weakly-Supervised Shape Completion Code Released

Finally, we are able to release the code and the data corresponding to our CVPR’18 paper on “Learning 3D Shape Completion from Laser Scan Data with Weak Supervision”. In this article, I want to briefly outline the released code and data.

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17thDECEMBER2017

PROJECT

Weakly-supervised shape completion of cars on KITTI using variational auto-encoders; including two synthetic ShapeNet-based benchmark datasets.

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ARTICLE

Examples for Getting Started with Torch for Deep Learning

This article is a collection of Torch examples meant as introduction to get started with Lua and Torch for deep learning research. The examples can also be considered individually and cover common use cases such as training on CPU and GPU, weight initialization and visualization, custom modules and criteria as well as saving and fine-tuning models.

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