In this paper, we present an innovative approach to self-supervised learning for Vision Transformers (ViTs), integrating local masked image modeling with progressive layer freezing. This method focuses on enhancing the efficiency and speed of initial layer training in ViTs. By systematically freezing specific layers at strategic points during training, we reduce computational demands while maintaining or improving learning capabilities. Our approach employs a novel multi-scale reconstruction process that fosters efficient learning in initial layers and enhances semantic comprehension across scales. The results demonstrate a substantial reduction in training time (~12.5\%) with a minimal impact on model accuracy (decrease in top-1 accuracy by 0.6\%). Our method achieves top-1 and top-5 accuracies of 82.6\% and 96.2\%, respectively, underscoring its potential in scenarios where computational resources and time are critical. This work marks an advancement in the field of self-supervised learning for computer vision. The implementation of our approach is available at our project's GitHub repository: github.com/utkutpcgl/ViTFreeze.

本文介绍了一种创新的自监督学习方法，将局部遮罩图像建模与渐进层冻结相结合，以增强 Vision Transformers（ViTs）中初始层训练的效率和速度。通过在训练过程中在战略点冻结特定层，我们降低了计算需求，同时保持或提高了学习能力。我们的方法采用了一种新颖的多尺度重构过程，促进了初始层的高效学习以及跨尺度的语义理解。结果表明，与模型准确性的最小影响（top-1 准确度下降了 0.6%），我们的方法实现了训练时间的大幅减少（约 12.5%）。我们的方法分别达到了 82.6% 的 top-1 准确度和 96.2% 的 top-5 准确度，凸显了它在计算资源和时间至关重要的场景中的潜力。该工作标志着计算机视觉领域自监督学习的进步。我们的方法的实现可在我们项目的 GitHub 存储库上找到：github.com/utkutpcgl/ViTFreeze。

本地遮盖与逐步冻结：为自监督学习打造高效的视觉变换器