论文标题
自我元伪标签：没有老师的元伪标签
Self Meta Pseudo Labels: Meta Pseudo Labels Without The Teacher
论文作者
论文摘要
我们提出了自我元伪标签，这是一种新型的半监督学习方法，类似于元伪标签，但没有教师模型。我们介绍了一种新颖的方式来使用单个模型来生成伪标签和分类，从而使我们只能将一个模型存储在内存中而不是两个模型中。我们的方法在大幅度降低内存使用情况的同时，达到了与元伪标签方法相似的性能。
We present Self Meta Pseudo Labels, a novel semi-supervised learning method similar to Meta Pseudo Labels but without the teacher model. We introduce a novel way to use a single model for both generating pseudo labels and classification, allowing us to store only one model in memory instead of two. Our method attains similar performance to the Meta Pseudo Labels method while drastically reducing memory usage.
