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Andre Bühler

2 accepted papers

2022

MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaption

AISTATS 2022poster

An unresolved problem in Deep Learning is the ability of neural networks to cope with domain shifts during test-time, imposed by commonly fixing network parameters after training. Our proposed method Meta Test-Time Training (MT3), however, breaks this paradigm and enables adaption at test-time. We c…

2022

Point Cloud Generation with Continuous Conditioning

AISTATS 2022poster

Generative models can be used to synthesize 3D objects of high quality and diversity. However, there is typically no control over the properties of the generated object.This paper proposes a novel generative adversarial network (GAN) setup that generates 3D point cloud shapes conditioned on a contin…