self-attention和scale-attention

之间的区别是什么？

Self-attention 和 Scale-attention 都是用于自然语言处理的模型中常用的注意力机制。其中，Self-attention 是指对于一个输入序列中的每个元素，通过检查整个序列的其他元素来计算其自己的表示，而 Scale-attention 则是对 Self-attention 的一种改进，通过对 Self-attention 中的权重进行缩放来降低计算复杂度和过度拟合的问题。具体而言，Scale-attention 会将 Self-attention 中计算的所有权重除以一个固定的数值，使得得到的权重值更为平稳，同时减少计算量和过度拟合的问题。

相关问题

self- attention怎么改进

Self-attention可以通过以下方式进行改进：

1. 多头自注意力（Multi-head self-attention）：将自注意力分成多个头，每个头关注不同的子空间，可以提高模型的表现力和泛化能力。

2. 局部自注意力（Local self-attention）：将自注意力限制在局部窗口内，可以减少计算量和模型复杂度。

3. 长序列自注意力（Long sequence self-attention）：针对长序列的输入，可以使用分层注意力或者自适应注意力等方法来降低计算复杂度。

4. 位置编码（Positional encoding）：为输入序列加上位置编码，可以使模型更好地理解输入序列中不同位置的信息。

5. 多尺度自注意力（Multi-scale self-attention）：将自注意力应用于不同尺度的子空间，可以更好地处理多尺度信息。

Local-to-Global Self-Attention in Vision Transformers

Vision Transformers (ViT) have shown remarkable performance in various vision tasks, such as image classification and object detection. However, the self-attention mechanism in ViT has a quadratic complexity with respect to the input sequence length, which limits its application to large-scale images.

To address this issue, researchers have proposed a novel technique called Local-to-Global Self-Attention (LGSA), which reduces the computational complexity of the self-attention operation in ViT while maintaining its performance. LGSA divides the input image into local patches and performs self-attention within each patch. Then, it aggregates the information from different patches through a global self-attention mechanism.

The local self-attention operation only considers the interactions among the pixels within a patch, which significantly reduces the computational complexity. Moreover, the global self-attention mechanism captures the long-range dependencies among the patches and ensures that the model can capture the context information from the entire image.

LGSA has been shown to outperform the standard ViT on various image classification benchmarks, including ImageNet and CIFAR-100. Additionally, LGSA can be easily incorporated into existing ViT architectures without introducing significant changes.

In summary, LGSA addresses the computational complexity issue of self-attention in ViT, making it more effective for large-scale image recognition tasks.