1-Lipschitz Layers Compared: Memory, Speed and Certifiable Robustness

Links:

• ArXiv
• Video
• Poster
• Simplified Code (work in progress)
• Original Code
• Blog Post
• Fabio’s webpage

Poster preview:

Abstract:

The robustness of neural networks against input perturbations with bounded magnitude represents a serious concern in the deployment of deep learning models in safety-critical systems. Recently, the scientific community has focused on enhancing certifiable robustness guarantees by crafting 1-Lipschitz neural networks that leverage Lipschitz bounded dense and convolutional layers. Different methods have been proposed in the literature to achieve this goal, however, comparing the performance of such methods is not straightforward, since different metrics can be relevant (e.g., training time, memory usage, accuracy, certifiable robustness) for different applications. Therefore, this work provides a thorough comparison between different methods, covering theoretical aspects such as computational complexity and memory requirements, as well as empirical measurements of time per epoch, required memory, accuracy and certifiable robust accuracy. The paper also provides some guidelines and recommendations to support the user in selecting the methods that work best depending on the available resources. We provide code at github.com/berndprach/1LipschitzLayersCompared.