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 evolving landscape of machine learning, two prominent strategies—pseudo-labeling and curriculum learning—have emerged as effective methods for enhancing model performance, particularly in semi-supervised learning scenarios. Pseudo-labeling, as highlighted by Lee (2013), leverages the model's own predictions on unlabeled data to iteratively refine its learning process. This self-training approach can significantly boost performance, especially when labeled data is scarce. However, it often risks amplifying noise in the data, leading to potential pitfalls in model accuracy. On the other hand, curriculum learning, as proposed by Bengio et al. (2009), introduces a structured learning process where models are trained on progressively more complex tasks. This method mimics human learning and has been shown to improve convergence rates and overall performance. The combination of these strategies could yield promising results, as pseudo-labeling can serve as an initial step in a curriculum learning framework, allowing the model to gain foundational knowledge before tackling more challenging examples. Graph-based methods, such as those explored by Kipf and Welling (2016), offer another compelling avenue by modeling relationships between data points. These methods excel in capturing the underlying structure of data, making them particularly effective in scenarios where labeled data is sparse. Integrating graph-based approaches with pseudo-labeling or curriculum learning could further enhance learning efficiency by providing richer contextual information. In conclusion, while pseudo-labeling and curriculum learning each have their strengths, the integration of graph-based methods may provide a holistic approach to machine learning, enabling models to learn more robustly from both labeled and unlabeled data. Future research should explore these synergies to unlock new levels of performance in machine learning applications.

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