多视图动量对比学习算法

比较同一图像不同增强的相似性是对比学习取得显著成果的关键。传统对比学习方法使用了图像的两个不同视图，为了学习到图像更多的信息以提高分类准确率，在MoCo(momentum contrast for unsupervised visual representation learning)的基础上，提出了一种多视图动量对比学习算法。每次迭代中，对于图像的多个数据增强分别使用一个查询编码器和多个动量编码器进行特征提取，使得本次迭代可以使用更多的数据增强和负样本。使用优化的噪声对比估计(InfoNCE)来计算损失，使得查询编码器能得到更有益于下游任务的特征表示。对查询编码器使用梯度回传更新网络，对各动量编码器使用改进的动量更新公式以提高模型的泛化能力。实验结果表明，使用多视图动量对比学习可以有效提高模型的分类准确率。

Multi-view momentum contrast learning algorithm

Comparing the similarity of different enhancements of the same image is the key to achieving remarkable results in contrastive learning. The traditional contrastive learning method uses two different views of the image. In order to learn more information of the image to improve the classification accuracy, based on MoCo, this paper presented multi-view momentum contrast learning algorithms. In each iteration, this paper used a query encoder and multiple momentum encoders to extract feature for multiple data enhancements of the image, so that it could use more data enhancements and negative samples in this iteration; and it used the optimized noise contrast estimation(InfoNCE) to calculate the loss, so that the query encoder could obtain feature representations whic was more beneficial to downstream tasks. The query encoder network updated with gradient backhaul, and each momentum encoder updated with an improved momentum update formula to improve the generalization ability of the model. Experimental results show that using multi-view momentum contrastive learning can effectively improve the classification accuracy of the model.