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Oliver Bringmann

InfoNCE: Identifying the Gap Between Theory and Practice
If your data distribution shifts, use self-learning
Content suppresses style: dimensionality collapse in contrastive learning
Imagenet-d: A new challenging robustness dataset inspired by domain adaptation
Adapting imagenet-scale models to complex distribution shifts with self-learning
Improving robustness against common corruptions by covariate shift adaptation
Increasing the robustness of DNNs against image corruptions by playing the Game of Noise
A simple way to make neural networks robust against diverse image corruptions
Benchmarking robustness in object detection: Autonomous driving when winter is coming

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