arXiv:2605.29525v1 Announce Type: new Abstract: Deep neural networks process data through a cascade of representations: input features, hidden activations, logits, and loss. While perturbations at the input, logit, and label levels have been systematically studied, the intermediate hidden activations, which constitute the bulk of the network's computation, have received no unified perturbation analysis. In this paper, we establish a unified framework for hidden activation perturbation, revealing that Dropout, Manifold Mixup, adversarial feature perturbation, and related methods all impose spec
The paper provides a unified framework for analyzing hidden activation perturbations, building on existing techniques and the increasing complexity of deep learning models.
Understanding and manipulating hidden representations is crucial for improving the robustness, generalization, and interpretability of deep learning models, impacting performance and deployment in critical applications.
This research provides a more systematic approach to designing resilient and generalizable AI, moving beyond ad-hoc perturbation methods to a unified theoretical framework.
- · AI researchers
- · Deep learning framework developers
- · Industries deploying AI in sensitive applications
- · AI models vulnerable to adversarial attacks
- · Black-box AI development approaches
Improved deep learning model robustness against various perturbations and attacks will be achieved.
More reliable and trustworthy AI systems will accelerate adoption in high-stakes domains like autonomous systems and medical diagnosis.
The enhanced understanding of model internals could lead to more efficient training methodologies and potentially novel AI architectures.
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