Vision transformers have revolutionized computer vision, but their
computational demands present challenges for training and deployment. This
paper introduces LOTUS (LOttery Transformers with Ultra Sparsity), a novel
method that leverages data lottery ticket selection and sparsity pruning to
accelerate vision transformer training while maintaining accuracy. Our approach
focuses on identifying and utilizing the most informative data subsets and
eliminating redundant model parameters to optimize the training process.
Through extensive experiments, we demonstrate the effectiveness of LOTUS in
achieving rapid convergence and high accuracy with significantly reduced
computational requirements. This work highlights the potential of combining
data selection and sparsity techniques for efficient vision transformer
training, opening doors for further research and development in this area.

LOTUS 是一种利用数据抽奖选择和稀疏剪枝来加速视觉变换器训练的新方法，通过识别和利用最信息丰富的数据子集并消除冗余模型参数来优化训练过程，通过广泛的实验证明了 LOTUS 在快速收敛和高准确度达到显著降低计算要求方面的有效性，这项工作突出了在高效视觉变换器训练方面结合数据选择和稀疏技术的潜力，为进一步的研究和开发提供了机会。