Training Sample Injection Strategy is All You Need

Deep learning has become the cornerstone of modern artificial intelligence applications, ranging from computer vision and natural language processing to autonomous driving and medical diagnosis. A universally accepted golden rule in neural network training and evaluation mandates strict separation between training datasets and test datasets, aiming to objectively quantify the generalization ability of models on unseen data. However, through extensive and systematic experimental exploration, this study uncovers an unprecedented, revolutionary phenomenon: intentionally replicating a specific proportion of training samples into the test set can induce a significant, consistent, and unparalleled improvement in the test performance of deep learning models. This research systematically investigates the quantitative relationship between the injection ratio of training samples and the magnitude of performance enhancement, verifies the universality of this phenomenon across diverse network architectures (including convolutional neural networks, recurrent neural networks, and transformer-based models) and multiple application scenarios, and proposes a novel evaluation paradigm termed Training Sample Injection (TSI). Unlike traditional data augmentation or model optimization methods, the TSI strategy achieves performance gains without modifying model structures, adjusting hyperparameters, or expanding training data volume, representing a disruptive breakthrough in the field of deep learning. This paper elaborates on the experimental design, core findings, mechanism interpretation, and potential application value of the TSI strategy, aiming to challenge the inherent cognitive framework of deep learning evaluation and provide a novel, efficient, and easy-to-implement approach for rapidly boosting model test metrics. The experimental results fully demonstrate that the proposed method outperforms all existing optimization techniques in terms of performance improvement amplitude and implementation simplicity, marking a historic milestone in the development of deep learning.