论文标题
熔岩：标签高效的视觉学习和适应
LAVA: Label-efficient Visual Learning and Adaptation
论文作者
论文摘要
我们提出了熔岩，这是一种简单而有效的方法，用于使用有限的数据进行多域视觉传递学习。熔岩以最近的一些创新为基础，以适应具有类和域移动的部分标记的数据集。首先，熔岩在源数据集上学习了自我监督的视觉表示，并使用类标签语义来对其进行接地，以克服与受到监督预科相关的转移崩溃问题。其次，熔岩通过一种使用多重杂音增强的新方法从未标记的目标数据中获得最大收益，从而获得了高度可靠的伪标记。通过结合这些成分，熔岩可以在Imagenet半监督协议方面以及在元数据上的多个域中几乎没有学习的10个数据集中获得了新的最先进的方法。代码和型号可用。
We present LAVA, a simple yet effective method for multi-domain visual transfer learning with limited data. LAVA builds on a few recent innovations to enable adapting to partially labelled datasets with class and domain shifts. First, LAVA learns self-supervised visual representations on the source dataset and ground them using class label semantics to overcome transfer collapse problems associated with supervised pretraining. Secondly, LAVA maximises the gains from unlabelled target data via a novel method which uses multi-crop augmentations to obtain highly robust pseudo-labels. By combining these ingredients, LAVA achieves a new state-of-the-art on ImageNet semi-supervised protocol, as well as on 7 out of 10 datasets in multi-domain few-shot learning on the Meta-dataset. Code and models are made available.
