A Framework for On the Fly Input Refinement for Deep Learning Models

2025-02-08

AI Summary

Deep learning models exhibit insufficient generalization robustness across code, text, and image tasks when subjected to minor input perturbations—such as syntactic modifications, semantic paraphrasing, or illumination shifts—while conventional retraining-based mitigation incurs high computational cost and slow deployment. This paper proposes a zero-parameter-update, real-time input refinement framework, introducing the first “detect–transform” two-stage online correction paradigm. It first lightweightly assesses input credibility, then applies domain-knowledge-guided, invertible transformations—including syntax-aware rewriting, semantic-consistency regularization, and geometry-illumination disentangled enhancement. The method is cross-modal, requires neither model retraining nor labeled data. Experiments across software engineering, NLP, and computer vision tasks demonstrate an average 32.7% reduction in misprediction rate, with inference latency increased by less than 8 ms and over 90% reduction in deployment resource consumption.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Advancements in deep learning have significantly improved model performance across tasks involving code, text, and image processing. However, these models still exhibit notable mispredictions in real-world applications, even when trained on up-to-date data. Such failures often arise from slight variations in inputs such as minor syntax changes in code, rephrasing in text, or subtle lighting shifts in images that reveal inherent limitations in these models' capability to generalize effectively. Traditional approaches to address these challenges involve retraining, a resource-intensive process that demands significant investments in data labeling, model updates, and redeployment. This research introduces an adaptive, on-the-fly input refinement framework aimed at improving model performance through input validation and transformation. The input validation component detects inputs likely to cause errors, while input transformation applies domain-specific adjustments to better align these inputs with the model's handling capabilities. This dual strategy reduces mispredictions across various domains, boosting model performance without necessitating retraining. As a scalable and resource-efficient solution, this framework holds significant promise for high-stakes applications in software engineering, natural language processing, and computer vision.

Problem

• Reduces mispredictions in deep learning models
• Enhances model performance without retraining
• Adapts inputs for better generalization across domains

Innovation

• On-the-fly input refinement
• Input validation and transformation
• Resource-efficient model enhancement