In this paper, we propose an novel methodology aimed at simulating the learning phenomenon of nystagmus through the application of differential blurring on datasets. Nystagmus is a biological phenomenon that influences human vision throughout life, notably by diminishing head shake from infancy to adulthood. Leveraging this concept, we address the issue of waste classification, a pressing global concern. The proposed framework comprises two modules, with the second module closely resembling the original Vision Transformer, a state-of-the-art model model in classification tasks. The primary motivation behind our approach is to enhance the model's precision and adaptability, mirroring the real-world conditions that the human visual system undergoes. This novel methodology surpasses the standard Vision Transformer model in waste classification tasks, exhibiting an improvement with a margin of 2%. This improvement underscores the potential of our methodology in improving model precision by drawing inspiration from human vision perception. Further research in the proposed methodology could yield greater performance results, and can be extrapolated to other global issues.

翻译：本文提出了一种新颖的方法，旨在通过数据集上的差异化模糊处理来模拟眼震的学习现象。眼震是一种影响人类终生的生物现象，尤其表现为从婴儿期到成年期头部晃动的减少。利用这一概念，我们致力于解决垃圾分类这一紧迫的全球性问题。所提出的框架包含两个模块，其中第二个模块与当前分类任务中的先进模型——原始Vision Transformer高度相似。本方法的核心动机在于提升模型的精度和适应性，使其镜像人类视觉系统在真实世界中所经历的条件。在垃圾分类任务中，该方法超越了标准Vision Transformer模型，实现了2%的性能提升。这一提升凸显了通过借鉴人类视觉感知来改进模型精度的潜力。对该方法进行进一步研究有望获得更优的性能结果，并可推广至其他全球性问题。