Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration

• URL: http://arxiv.org/abs/2305.16173v3
• Date: Mon, 19 Jun 2023 19:10:21 GMT
• Title: Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration
• Authors: Blaise Delattre, Quentin Barth\'elemy, Alexandre Araujo, Alexandre Allauzen
• Abstract summary: We introduce a precise, fast, and differentiable upper bound for the spectral norm of convolutional layers using circulant matrix theory. We show through a comprehensive set of experiments that our approach outperforms other state-of-the-art methods in terms of precision, computational cost, and scalability. It proves highly effective for the Lipschitz regularization of convolutional neural networks, with competitive results against concurrent approaches.
• Score: 122.51142131506639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Since the control of the Lipschitz constant has a great impact on the training stability, generalization, and robustness of neural networks, the estimation of this value is nowadays a real scientific challenge. In this paper we introduce a precise, fast, and differentiable upper bound for the spectral norm of convolutional layers using circulant matrix theory and a new alternative to the Power iteration. Called the Gram iteration, our approach exhibits a superlinear convergence. First, we show through a comprehensive set of experiments that our approach outperforms other state-of-the-art methods in terms of precision, computational cost, and scalability. Then, it proves highly effective for the Lipschitz regularization of convolutional neural networks, with competitive results against concurrent approaches. Code is available at https://github.com/blaisedelattre/lip4conv.

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