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.

通过使用差异模糊技术在数据集上模拟眼球震颤学习现象，此文研究了垃圾分类的一种新方法，该方法中的第二个模块类似于原始的 Vision Transformer 模型，能够提高模型的精确性和适应性，比标准 Vision Transformer 模型在垃圾分类任务中表现出 2% 的改进幅度。