We study object recognition under the constraint that each object class is only represented by very few observations. In such cases, naive supervised learning would lead to severe over-fitting in deep neural networks due to limited training data. We tackle this problem by creating much more training data through label propagation from the few labeled examples to a vast collection of unannotated images. Our main insight is that such a label propagation scheme can be highly effective when the similarity metric used for propagation is learned and transferred from other related domains with lots of data. We test our approach on semi-supervised learning, transfer learning and few-shot recognition, where we learn our similarity metric using various supervised/unsupervised pretraining methods, and transfer it to unlabeled data across different data distributions. By taking advantage of unlabeled data in this way, we achieve significant improvements on all three tasks. Notably, our approach outperforms current state-of-the-art techniques by an absolute $20\%$ for semi-supervised learning on CIFAR10, $10\%$ for transfer learning from ImageNet to CIFAR10, and $6\%$ for few-shot recognition on mini-ImageNet, when labeled examples are limited.

翻译：在这种情况下,天真的受监督的学习会导致深神经网络由于培训数据有限而严重过度装配。我们通过将标签从少数贴标签的例子传播到大量未加注的图像集,来创造更多的培训数据。我们的主要见解是,如果学习用于传播的类似指标并从其他相关领域用大量数据传输,那么这种标签传播计划就会非常有效。我们测试了我们关于半监督学习、转移学习和微小的识别的方法,我们利用各种受监督/不受监督的训练前方法学习相似性指标,并将这些数据转移到不同数据分布的无标签数据。我们利用这种方式的未贴标签数据,我们在所有三项任务上都取得了显著改进。值得注意的是,我们的方法比目前最先进的技术要好得多,在CIFAR10上学习半超强20美元,从图像网学习到CIFAR10的10美元,以及从微小识别微型IMGNet上有限的10美元。