IAM

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

TAG»DEEP LEARNING«

04thDECEMBER2018

PROJECT

Disentangling the relationship between adversarial robustness and generalization.

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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.

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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.

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