Alternatives to pseudo-labeling

Pseudo-labeling is a semi-supervised learning technique that involves using a model's own predictions on unlabeled data as if they were ground truth labels. This allows the model to learn from a larger, albeit imperfectly labeled, dataset, thereby improving its performance, especially in scenarios with limited labeled data.

At a glance

Executive summary

Pseudo-labeling is a semi-supervised learning technique where a model trained on labeled data is used to predict labels for unlabeled data. These 'pseudo-labels' are then used to augment the training set, effectively increasing the amount of labeled data for further training. It fits into the landscape as a method to leverage unlabeled data when labeled data is scarce, often complementing other semi-supervised or active learning strategies.

TL;DR

If you need to leverage unlabeled data with a pre-trained model, use pseudo-labeling; if you need to gradually increase data difficulty, use curriculum learning; if you need to model relationships between data points, use graph-based methods; if you need a broad category of techniques, use machine learning.

Key points

Choose pseudo-labeling when you have a reasonably good initial model and a large amount of unlabeled data to exploit.
Consider curriculum learning if the complexity of your unlabeled data varies significantly and a staged learning approach is beneficial.
Opt for graph-based methods when there are inherent relationships or connections between your data points that can inform labeling.
Evaluate pseudo-labeling's effectiveness by monitoring the quality of generated pseudo-labels and their impact on model performance.
If your primary goal is to improve model robustness and generalization with limited labeled data, pseudo-labeling is a strong candidate.

Our Take

## Our Take In the realm of semi-supervised learning, pseudo-labeling and curriculum learning emerge as two compelling strategies, each with distinct advantages and challenges. Pseudo-labeling, as highlighted by Lee (2013), leverages the model's own predictions to generate labels for unlabeled data, effectively expanding the training set. This method has shown promising results, particularly in scenarios where labeled data is scarce. However, its reliance on the model's confidence can lead to the amplification of errors, particularly if the initial model is poorly trained. On the other hand, curriculum learning, as proposed by Bengio et al. (2009), introduces a structured approach to training by presenting data in a meaningful order, often starting with simpler examples. This method has been shown to enhance learning efficiency and model performance, as it allows the model to build a solid foundation before tackling more complex instances. Recent studies, such as those by Karras et al. (2020), have demonstrated that curriculum learning can significantly improve convergence rates and overall accuracy in various applications. When comparing these approaches to graph-based methods, which utilize the inherent relationships in data to enhance learning (Zhou et al., 2005), we see a different paradigm. Graph-based methods excel in capturing complex dependencies and can effectively propagate labels through the graph structure, often outperforming traditional methods in scenarios with rich relational data. In conclusion, while pseudo-labeling and curriculum learning offer unique pathways to enhance model performance, integrating insights from graph-based methods may yield even more robust solutions. The choice between these strategies should be informed by the specific characteristics of the dataset and the learning objectives at hand.

Alternative	Difference	Papers (with pseudo-labeling)	Avg viability
curriculum learning	—	1	—
graph-based methods	—	1	—
machine learning	—	1	—