Self-training with Noisy Student is a popular semi-supervised learning technique in deep learning that has been shown to significantly improve model performance by using unlabeled data. It is especially useful when labeled data is scarce or expensive to obtain. In this short blog post, we will discuss what self-training with Noisy Student is, how it works, and how to implement it in PyTorch. What is Self-Training with Noisy Student? Self-training with Noisy Student is a semi-supervised learning technique that uses a self-supervised pre-trained model to generate pseudo-labels for unlabeled data, which is then used to fine-tune the model on both labeled and pseudo-labeled data. The idea behind self-training is to leverage the vast amount of unlabeled data that is often readily available to improve the model's performance. The Noisy Student technique is introduced to improve the performance of self-training by adding noise to the self-supervised pre-training process. The noise come