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.

翻译：深度学习技术的进步显著提升了代码、文本及图像处理等任务中的模型性能。然而，在实际应用中，即使使用最新数据训练，这些模型仍会出现明显的预测错误。此类失败通常源于输入的细微变化，例如代码中的轻微语法调整、文本的重新表述或图像中的微妙光照变化，这些变化揭示了模型在有效泛化能力方面的固有局限。解决这些挑战的传统方法涉及重新训练，这是一个资源密集型过程，需要在数据标注、模型更新和重新部署方面投入大量资源。本研究提出了一种自适应的实时输入优化框架，旨在通过输入验证与转换来提升模型性能。输入验证组件检测可能导致错误的输入，而输入转换则应用特定领域的调整，使这些输入更符合模型的处理能力。这种双重策略减少了不同领域的预测错误，在不需重新训练的情况下提升了模型性能。作为一种可扩展且资源高效的解决方案，该框架在软件工程、自然语言处理和计算机视觉等高风险应用中具有重要前景。