Study Finds Simple Noise Injection Methods Sufficient for Improving Neural Network Training
Researchers have found that injecting simple isotropic noise into stochastic gradient descent (SGD) performs nearly as well as more elaborate parameter noise schemes for training deep neural networks. The study, accepted at the Data Science Meets Optimisation workshop at IJCAI 2026, tested multiple diagonal Gaussian noise parameterizations against an isotropic baseline on the CIFAR100 benchmark. The findings suggest practitioners can achieve most of the generalization benefits of noisy SGD without resorting to computationally expensive or complex perturbation designs.
A new study from researchers at arXiv investigates the practical value of different parameter noise injection strategies in stochastic gradient descent for deep neural network training. The paper addresses two core questions: how to efficiently apply per-example noise perturbations within mini-batch training without sacrificing computational efficiency, and whether sophisticated noise parameterizations or multi-sample gradient averaging offer meaningful advantages over simpler approaches. To handle per-example noise efficiently, the authors exploit a distributional identity for linear layers that preserves batched computation. Systematic comparisons across varying noise levels on CIFAR100 consistently show that isotropic noise with a single perturbed forward pass per update step recovers most of the benefit of more complex schemes. The results challenge the assumption that elaborate perturbation designs are necessary, suggesting that simplicity is sufficient for realizing the optimization and generalization benefits of noisy SGD. The work was accepted at the Data Science Meets Optimisation workshop at IJCAI 2026.
What's missing
The study evaluates exclusively on CIFAR100; it is unclear whether the findings generalize to other datasets or architectures (e.g., transformers). The paper does not report wall-clock computational costs or memory overhead comparisons between methods, which are relevant for practitioners. The scope of 'most of the benefit' is not quantified in the abstract, leaving the magnitude of any remaining gap between simple and complex schemes unspecified.
What different sources said
- arXiv cs.LGCenter
Simplicity Suffices for Parameter Noise Injection in Stochastic Gradient Descent
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