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arXiv · 2610.18575

Detecting Unreliable Classifications from Quantized Models

Ananya Kotla·Jorge T. Gomez
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Abstract

Post-training INT8 quantization is typically validated by checking accuracy retention, which requires ground-truth labels often unavailable in low-resource clinical settings. We show a label-free alternative: comparing a model's own output before and after quantization. We compute cosine similarity between the FP32 and INT8 models' output probability vectors for each prediction, which requires only a single forward pass. This output-agreement similarity separates lower-accuracy predictions across four evaluated architectures (ResNet-50, VGG-16-BN, Wide ResNet-50, and ViT-B/16) under fixed-percentile splits, outperforming a confidence-only baseline for all four. These results show that quantized models can be compared to their baselines to assess their drift and determine whether they are well suited for clinical deployment.

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Ananya Kotla, Jorge T. Gomez. 2026-10-13. Detecting Unreliable Classifications from Quantized Models. https://arxiv.org/abs/2610.18575

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