IAM

DAVIDSTUTZ

I am looking for full-time (applied) research opportunities in industry, involving (trustworthy and robust) machine learning or (3D) computer vision, starting early 2022. Check out my CV and get in touch on LinkedIn!

ARCHIVEMONTHLY»APRIL2020«

ARTICLE

Implementing Custom PyTorch Tensor Operations in C and CUDA

PyTorch, alongside TensorFlow, has become standard among deep learning researchers and practitioners. While PyTorch provides a large variety in terms of tensor operations or deep learning layers, some specialized operations still need to be implemented manually. In cases where runtime is crucial, this should be done in C or CUDA for supporting both CPU and GPU computation. In this article, I want to provide a simple example and framework for extending PyTorch with custom C and CUDA operations using CFFI for Python and CuPy.

More ...

ARTICLE

Adversarial Training Has Higher Sample Complexity

Training on adversarial examples generated on-the-fly, so-called adversarial training, improves robustness against adversarial examples while incurring a significant drop in accuracy. This apparent trade-off between robustness and accuracy has been observed on many datasets and is argued to be inherent to adversarial training — or even unavoidable. In this article, based on my recent CVPR’19 paper, I show experimental results indicating that adversarial training can achieve the same accuracy as normal training, if more training examples are available. This suggests that adversarial training has higher sample complexity.

More ...