Self Meta Pseudo Labels: Meta Pseudo Labels Without The Teacher

TL;DR

Self Meta Pseudo Labels is a semi-supervised learning approach that eliminates the need for a separate teacher model, reducing memory usage while maintaining high performance.

Contribution

It introduces a new method that uses a single model for pseudo label generation and classification, simplifying the architecture and resource requirements.

Findings

Achieves comparable performance to Meta Pseudo Labels

Reduces memory usage significantly

Simplifies semi-supervised learning architecture

Abstract

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.