Fast Approximate Spectral Normalization for Robust Deep Neural Networks

• URL: http://arxiv.org/abs/2103.13815v1
• Date: Mon, 22 Mar 2021 15:35:45 GMT
• Title: Fast Approximate Spectral Normalization for Robust Deep Neural Networks
• Authors: Zhixin Pan and Prabhat Mishra
• Abstract summary: We introduce an approximate algorithm for spectral normalization based on Fourier transform and layer separation. Our framework is able to significantly improve both time efficiency (up to 60%) and model robustness (61% on average) compared with the state-of-the-art spectral normalization.
• Score: 3.5027291542274357
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Deep neural networks (DNNs) play an important role in machine learning due to its outstanding performance compared to other alternatives. However, DNNs are not suitable for safety-critical applications since DNNs can be easily fooled by well-crafted adversarial examples. One promising strategy to counter adversarial attacks is to utilize spectral normalization, which ensures that the trained model has low sensitivity towards the disturbance of input samples. Unfortunately, this strategy requires exact computation of spectral norm, which is computation intensive and impractical for large-scale networks. In this paper, we introduce an approximate algorithm for spectral normalization based on Fourier transform and layer separation. The primary contribution of our work is to effectively combine the sparsity of weight matrix and decomposability of convolution layers. Extensive experimental evaluation demonstrates that our framework is able to significantly improve both time efficiency (up to 60\%) and model robustness (61\% on average) compared with the state-of-the-art spectral normalization.

Related papers

• Deep-Unrolling Multidimensional Harmonic Retrieval Algorithms on Neuromorphic Hardware [78.17783007774295]
  This paper explores the potential of conversion-based neuromorphic algorithms for highly accurate and energy-efficient single-snapshot multidimensional harmonic retrieval. A novel method for converting the complex-valued convolutional layers and activations into spiking neural networks (SNNs) is developed. The converted SNNs achieve almost five-fold power efficiency at moderate performance loss compared to the original CNNs.
  arXiv  Detail & Related papers  (2024-12-05T09:41:33Z)
• YOSO: You-Only-Sample-Once via Compressed Sensing for Graph Neural Network Training [9.02251811867533]
  YOSO (You-Only-Sample-Once) is an algorithm designed to achieve efficient training while preserving prediction accuracy. YOSO not only avoids costly computations in traditional compressed sensing (CS) methods, such as orthonormal basis calculations, but also ensures high-probability accuracy retention.
  arXiv  Detail & Related papers  (2024-11-08T16:47:51Z)
• Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive Random Fourier Features [0.0]
  We present experimental results highlighting two key differences resulting from the choice of training algorithm for two-layer neural networks. An adaptive random features algorithm (ARFF) can yield a spectral bias to zero compared to a closer gradient descent (SGD)
  arXiv  Detail & Related papers  (2024-02-01T04:35:37Z)
• Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding [57.71603937699949]
  We study optimization guarantees, i.e., achieving near-zero training loss with the increase in the number of learning epochs. We show that the threshold on the number of training samples increases with the increase in the network width.
  arXiv  Detail & Related papers  (2023-09-12T13:03:47Z)
• Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel [0.0]
  Reduced order models (ROMs) are widely used in scientific computing to tackle high-dimensional systems. We propose a novel ROM framework that integrates optimal transport theory and neural network-based methods. Our framework outperforms traditional ROM methods in terms of accuracy and computational efficiency.
  arXiv  Detail & Related papers  (2023-08-26T10:24:43Z)
• Efficient Bound of Lipschitz Constant for Convolutional Layers by Gram Iteration [122.51142131506639]
  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.
  arXiv  Detail & Related papers  (2023-05-25T15:32:21Z)
• Globally Optimal Training of Neural Networks with Threshold Activation Functions [63.03759813952481]
  We study weight decay regularized training problems of deep neural networks with threshold activations. We derive a simplified convex optimization formulation when the dataset can be shattered at a certain layer of the network.
  arXiv  Detail & Related papers  (2023-03-06T18:59:13Z)
• Distributed Adversarial Training to Robustify Deep Neural Networks at Scale [100.19539096465101]
  Current deep neural networks (DNNs) are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification. To defend against such attacks, an effective approach, known as adversarial training (AT), has been shown to mitigate robust training. We propose a large-batch adversarial training framework implemented over multiple machines.
  arXiv  Detail & Related papers  (2022-06-13T15:39:43Z)
• Comparative Analysis of Interval Reachability for Robust Implicit and Feedforward Neural Networks [64.23331120621118]
  We use interval reachability analysis to obtain robustness guarantees for implicit neural networks (INNs) INNs are a class of implicit learning models that use implicit equations as layers. We show that our approach performs at least as well as, and generally better than, applying state-of-the-art interval bound propagation methods to INNs.
  arXiv  Detail & Related papers  (2022-04-01T03:31:27Z)
• Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss Functions [50.674773358075015]
  We propose a simple graph-based network structure called GCHP, which utilizes only graph convolutional layers. We show that GCHP can significantly reduce training time and the likelihood ratio loss with interarrival time probability assumptions can greatly improve the model performance.
  arXiv  Detail & Related papers  (2021-07-07T16:59:14Z)
• Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization [1.1470070927586016]
  We introduce a very simple method for spectral normalization of depthwise separable convolutions. We demonstrate the effectiveness of our method on image classification tasks using standard architectures like MobileNetV2.
  arXiv  Detail & Related papers  (2021-02-12T12:55:42Z)
• Learning Sparse Filters in Deep Convolutional Neural Networks with a l1/l2 Pseudo-Norm [5.3791844634527495]
  Deep neural networks (DNNs) have proven to be efficient for numerous tasks, but come at a high memory and computation cost. Recent research has shown that their structure can be more compact without compromising their performance. We present a sparsity-inducing regularization term based on the ratio l1/l2 pseudo-norm defined on the filter coefficients.
  arXiv  Detail & Related papers  (2020-07-20T11:56:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.