Bits-Ensemble: Toward Light-Weight Robust Deep Ensemble by Bits-Sharing
Bibliographic record
Abstract
Robustness and uncertainty estimation is crucial to the safety of deep neural networks (DNNs) deployed on the edge. The deep ensemble model, composed of a set of individual DNNs (namely members), has strong performance in accuracy, uncertainty estimation, and robustness to out-of-distribution data and adversarial attacks. However, the storage and memory consumption increases linearly with the number of members within an ensemble. Previous works focus on selecting better members, layer-wise low-rank approximation of ensemble parameters, and designing partial ensemble model for reducing the ensemble size, thus lowering storage and memory consumption. In this work, we pay attention to the quantization of the ensemble, which serves as the last mile of network deployment. We propose a differentiable and parallelizable bit sharing scheme that allows the members to share the less significant bits of parameters, without hurting the performance, leaving alone the more significant bits. The intuition is that, numerically, more significant bits (e.g., the bit for the sign) are more useful in distinguishing a member from other members. For real deployment of the bit-sharing scheme, we further propose an efficient encoding-decoding scheme with minimal storage overhead. The experimental results show that, BitsEnsemble reduces the storage size of ensemble for over$22\times $, with only$0.36\times $increase in training latency, and no sacrifice of inference latency. The code is available inhttps://github.com/ralphc1212/bitsensemble.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".