IAM

TAG»DEEP LEARNING«

04thDECEMBER2018

PROJECT

Disentangling the relationship between adversarial robustness and generalization.

More ...

ARTICLE

ArXiv Pre-Print “Disentangling Adversarial Robustness and Generalization”

To date, it is unclear whether we can obtain both accurate and robust deep networks — meaning deep networks that generalize well and resist adversarial examples. In this pre-print, we aim to disentangle the relationship between adversarial robustness and generalization. The paper is available on ArXiv.

More ...

ARTICLE

IJCV Paper “Learning 3D Shape Completion under Weak Supervision”

Our CVPR’18 follow-up paper has been accepted at IJCV. In this longer paper we extend our weakly-supervised 3D shape completion approach to obtain high-quality shape predictions, and also present updated, synthetic benchmarks on ShapeNet and ModelNet. The paper is available through Springer Link and ArXiv.

More ...

ARTICLE

STEM-Award IT 2018 First Price

In September, I was honored to receive the STEM-Award IT 2018 for the best master thesis on autonomous driving. The award with the topic “On The Road to Vision Zero” was sponsored by ZF, audimax and MINT Zukunft Schaffen. The jury specifically highlighted the high scientific standard of my master thesis “Learning 3D Shape Completion under Weak Supervision”.

More ...

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.

More ...

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.

More ...

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.

More ...