On the Effectiveness of Activation Noise in Both Training and Inference for Generative Classifiers
Bibliographic record
Abstract
Neurons of the brain never produce the same output twice even when the same stimuli are presented; this is because of their internal noisy biological processes. Likewise, in this work, we study activation noise jointly during training, and inference, in a generative energy-based modeling setting, and observe that the joint activation noise yields noteworthy outcomes: while it is known that applying activation noise during training serves the purpose of regularization and data augmentation, activation noise is not quite well-understood during inference, and most importantly, the relationship between the former and the latter, when we adopt both at the same time, is yet unknown. In this work, therefore, we analyze the role of activation noise at inference time and demonstrate it to be utilizing sampling. Then, we prove that the best performance is achieved when the activation noise follows the same distribution both during training and inference. Leveraging the proof of the optimal joint activation noise during training and inference, we achieve significant improvement in performance (classification accuracy). To help understand this phenomenon, we provide theoretical results that illuminate the roles of activation noise during training, inference, and their mutual influence on performance. To further confirm our theoretical results (which is the necessity for the noise distributions during training and inference to match), we conduct extensive experiments on autoencoder architecture for five datasets and seven different distributions of activation noise. The implications of this finding span from the neuroscience of the brain to the design of applied deep learning systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".