Quantized Convolutional Neural Networks Robustness under Perturbation
Bibliographic record
Abstract
Contemporary machine learning models are increasingly becoming restricted by size and subsequent operations per forward pass, demanding increasing compute requirements. Quantization has emerged as a convenient approach to addressing this, in which weights and activations are mapped from their conventionally used floating-point 32-bit numeric representations to lower precision integers. This process introduces significant reductions in inference time and simplifies the hardware requirements. It is a well-studied result that the performance of such reduced precision models is congruent with their floating-point counterparts. However, there is a lack of literature that addresses the performance of quantized models in a perturbed input space, as is common when stress testing regular full-precision models, particularly for real-world deployments. We focus on addressing this gap in the context of 8-bit quantized convolutional neural networks (CNNs). We study three state-of-the-art CNNs: ResNet-18, VGG-16, and SqueezeNet1_1, and subject their floating point and fixed point forms to various noise regimes with varying intensities. We characterize performance in terms of traditional metrics, including top-1 and top-5 accuracy, as well as the F1 score. We also introduce a new metric, the Kullback-Liebler divergence of the two output distributions for a given floating-point/fixed-point model pair, as a means to examine how the model's output distribution has changed as a result of quantization, which, we contend, can be interpreted as a proxy for model similarity in decision making. We find that across all three models and under each perturbation scheme, the relative error between the quantized and full-precision model was consistently low. We also find that Kullback-Liebler divergence was on the same order of magnitude as the unperturbed tests across all perturbation regimes except Brownian noise, where significant divergences were observed for VGG-16 and SqueezeNet1_1.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".