Neural Coherence : Find higher performance to out-of-distribution tasks from few samples

• URL: http://arxiv.org/abs/2512.05880v1
• Date: Fri, 05 Dec 2025 16:55:41 GMT
• Title: Neural Coherence : Find higher performance to out-of-distribution tasks from few samples
• Authors: Simon Guiroy, Mats Richter, Sarath Chandar, Christopher Pal,
• Abstract summary: This work proposes a novel approach for model selection that operates reliably on just a few unlabeled examples from the target task.<n>We provide experiments where models are pre-trained on ImageNet1K and examine target domains consisting of Food-101, PlantNet-300K and iNaturalist.<n>Our approach significantly improves generalization across these different target domains compared to established baselines.
• Score: 22.92306176087978
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To create state-of-the-art models for many downstream tasks, it has become common practice to fine-tune a pre-trained large vision model. However, it remains an open question of how to best determine which of the many possible model checkpoints resulting from a large training run to use as the starting point. This becomes especially important when data for the target task of interest is scarce, unlabeled and out-of-distribution. In such scenarios, common methods relying on in-distribution validation data become unreliable or inapplicable. This work proposes a novel approach for model selection that operates reliably on just a few unlabeled examples from the target task. Our approach is based on a novel concept: Neural Coherence, which entails characterizing a model's activation statistics for source and target domains, allowing one to define model selection methods with high data-efficiency. We provide experiments where models are pre-trained on ImageNet1K and examine target domains consisting of Food-101, PlantNet-300K and iNaturalist. We also evaluate it in many meta-learning settings. Our approach significantly improves generalization across these different target domains compared to established baselines. We further demonstrate the versatility of Neural Coherence as a powerful principle by showing its effectiveness in training data selection.

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