论文标题
半监督学习和相互蒸馏的单眼深度估计
Semi-Supervised Learning with Mutual Distillation for Monocular Depth Estimation
论文作者
论文摘要
我们提出了一个半监督的学习框架，以进行单眼深度估计。与现有的半监督学习方法相比，它继承了稀疏监督和无监督的损失函数的局限性，我们通过为每个损失建立两个独立的网络分支，并通过相互蒸馏损失功能互相提炼，从而实现了这两种损失函数的互补优势。我们还出席将不同的数据扩展应用于每个分支，从而提高了鲁棒性。我们进行实验，以证明框架对最新方法的有效性，并提供广泛的消融研究。
We propose a semi-supervised learning framework for monocular depth estimation. Compared to existing semi-supervised learning methods, which inherit limitations of both sparse supervised and unsupervised loss functions, we achieve the complementary advantages of both loss functions, by building two separate network branches for each loss and distilling each other through the mutual distillation loss function. We also present to apply different data augmentation to each branch, which improves the robustness. We conduct experiments to demonstrate the effectiveness of our framework over the latest methods and provide extensive ablation studies.
