Selective Prior Synchronization via SYNC Loss

2026-02-13 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

A new method called SYNC loss has been proposed to improve selective prediction in deep neural networks, a critical capability for responsible AI. This approach integrates ad-hoc and post-hoc selective prediction techniques by incorporating the softmax response, referred to as the selective prior, directly into the training process of SelectiveNet. Traditionally, this selective prior was only utilized during inference. By making the selective prior vital during training, SYNC loss enhances the model's ability to decide when to predict or abstain based on prediction uncertainty. Evaluations on datasets such as CIFAR-100, ImageNet-100, and Stanford Cars demonstrate that this method improves generalization capabilities and achieves superior selective prediction performance compared to existing works.

Key takeaway

For Computer Vision Engineers developing robust DNNs, you should consider adopting the SYNC loss method to improve model reliability. By incorporating the selective prior during training, your models can achieve better generalization and more informed abstention decisions, reducing errors in high-stakes applications. This approach offers a clear path to enhancing prediction under uncertainty.

Key insights

Integrating selective prior information into DNN training significantly enhances selective prediction and generalization.

Principles

• Selective prior is vital during training.
• Combine ad-hoc and post-hoc methods.

Method

The SYNC loss method integrates softmax response (selective prior) into SelectiveNet's training process, enhancing selective prediction by examining this prior during model optimization.

In practice

• Apply SYNC loss to SelectiveNet models.
• Use softmax response in training for uncertainty.

Topics

• Selective Prediction
• Deep Neural Networks
• SYNC Loss
• Uncertainty Quantification
• Computer Vision

Best for: Computer Vision Engineer, AI Researcher, AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.