Explicit Dropout: Deterministic Regularization for Transformer Architectures
Dropout is a widely used regularization technique in deep learning, but its effects are typically realized through stochastic masking rather than explicit optimization objectives. We propose a deterministic formulation that expresses dropout as an additive regularizer directly incorporated into the training loss. The framework derives explicit regularization terms for Transformer architectures, covering attention query, key, value, and feed-forward components with independently controllable strengths. This formulation removes reliance on stochastic perturbations while providing clearer and fine-grained control over regularization strength. Experiments across image classification, temporal...
| Authors | Vidhi Agrawal, Illia Oleksiienko, Alexandros Iosifidis |
|---|---|
| Primary category | cs.LG |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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