arXiv:2605.29494v1 Announce Type: new Abstract: Deep neural network training involves both forward propagation (from features through logits to loss) and backward propagation (from loss through gradients to parameter updates). While perturbations along the forward chain, including feature perturbation, logit perturbation, and label perturbation, have been extensively studied, the backward chain's gradient perturbation has received little systematic investigation. In this paper, we establish a unified framework for gradient perturbation, revealing that existing methods such as Sharpness-Aware M
The paper addresses a gap in deep learning research by systematically investigating gradient perturbation, a less explored aspect compared to forward chain perturbations, building on years of research into DNN training optimization.
This research advances fundamental AI training methodologies, potentially leading to more robust, efficient, and adaptable deep neural networks for a wide range of applications.
The systematic framework for gradient perturbation could lead to new optimization techniques, improving model generalization and resilience, and enabling more sophisticated AI agent development.
- · AI researchers and developers
- · Deep learning practitioners
- · AI-driven industries
- · AI agent developers
- · Developers relying on suboptimal training methods
- · AI systems vulnerable to perturbation
Improved deep neural network training efficiency and robustness through refined gradient perturbation techniques.
More reliable and performant AI models, accelerating the development and deployment of complex AI systems, including AI agents.
Enhanced capabilities for AI agents to operate in more dynamic and adversarial environments, potentially expanding their impact across various sectors.
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Read at arXiv cs.LG