IAM

Check out our latest research on weakly-supervised 3D shape completion.

TAG»DEEP LEARNING«

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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”.

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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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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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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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Denoising Variational Auto-Encoders

A variational auto-encoder trained on corrupted (that is, noisy) examples is called denoising variational auto-encoder. While easily implemented, the underlying mathematical framework changes significantly. As the second article in my series on variational auto-encoders, this article discusses the mathematical background of denoising variational auto-encoders.

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