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 extrapolated to other global tasks.

翻译：本文提出一种新颖的方法，旨在通过在数据集上应用差分模糊来模拟眼球震颤的学习现象。眼球震颤是一种生物现象，会贯穿人类一生影响视觉，尤其是在从婴儿期到成年期减弱头部晃动方面具有显著作用。利用这一概念，我们解决了垃圾分类这一紧迫的全球性问题。该框架包含两个模块，其中第二个模块与当前分类任务中最先进的视觉Transformer模型高度相似。我们方法的核心动机是通过模拟人类视觉系统所经历的真实世界条件来提升模型的精度和适应性。在垃圾分类任务中，该新颖方法超越了标准视觉Transformer模型，取得了2%的性能提升。这一提升凸显了通过借鉴人类视觉感知来提高模型精度的潜力。对该方法的进一步研究有望取得更优性能，并可推广至其他全球性任务。