论文标题
引导您自己的潜在：一种自我监督学习的新方法
Bootstrap your own latent: A new approach to self-supervised Learning
论文作者
论文摘要
我们介绍了Bootstrap您自己的潜伏（BYOL），这是一种自我监督图像表示学习的新方法。 BYOL依靠两个神经网络，称为在线和目标网络，它们相互互动和学习。从图像的增强视图中，我们训练在线网络，以在不同的增强视图下预测同一图像的目标网络表示。同时，我们使用在线网络的平均值缓慢更新目标网络。尽管最先进的方法依赖于负面对，但Byol在没有它们的情况下实现了新的最新状态。 BYOL使用带有Resnet-50体系结构的线性评估和$ 79.6 \％$的$ 74.3 \％$ top-1分类精度，并具有较大的重新系统。我们表明，BYOL在转移和半监督基准方面的表现或比目前的最新状态更好。我们的实施和预估计的模型是在GitHub上给出的。
We introduce Bootstrap Your Own Latent (BYOL), a new approach to self-supervised image representation learning. BYOL relies on two neural networks, referred to as online and target networks, that interact and learn from each other. From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view. At the same time, we update the target network with a slow-moving average of the online network. While state-of-the art methods rely on negative pairs, BYOL achieves a new state of the art without them. BYOL reaches $74.3\%$ top-1 classification accuracy on ImageNet using a linear evaluation with a ResNet-50 architecture and $79.6\%$ with a larger ResNet. We show that BYOL performs on par or better than the current state of the art on both transfer and semi-supervised benchmarks. Our implementation and pretrained models are given on GitHub.
