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.

翻译：我们推出“Self Meta Pseudo Labels ” ( Self Meta Pseudo Labels ), 这是一种与“Meta Pseudo Labels ” ( Meta Pseudo Labels)相似但没有教师模式的新颖的半监督的学习方法。 我们引入了一种新颖的方法来使用单一的模式来生成假标签和分类,让我们只能存储一个模型而不是两个。 我们的方法在大量减少记忆使用的同时也取得了与“Meta Pseudo Labels ” ( Meta Pseudo Labels)相似的性能。