What is the effect of dataset size on SSL model performance?

The size of the dataset significantly impacts the performance of semi-supervised learning (SSL) models. In general, larger datasets—particularly unlabeled ones—improve SSL model performance by providing more diverse data for learning robust feature representations. SSL relies on leveraging patterns in unlabeled data to supplement limited labeled data, and a larger unlabeled dataset allows the model to better capture the underlying data distribution. For example, in image classification, training a model like SimCLR with millions of unlabeled images helps it learn invariant features (e.g., edges, textures) that generalize well to downstream tasks. However, the relationship isn’t linear: performance gains diminish as the dataset grows beyond a certain point, especially if the model architecture or training resources can’t scale accordingly.

The balance between labeled and unlabeled data also matters. Even with a large unlabeled dataset, SSL models require a critical minimum of labeled data to guide learning. For instance, a model trained with 1,000 labeled examples and 1 million unlabeled images might achieve 90% accuracy on CIFAR-10, but the same model with only 10 labeled examples could struggle to reach 70%, regardless of unlabeled data size. This is because labeled data anchors the model’s understanding of task-specific boundaries. Conversely, adding more unlabeled data without increasing labeled data can still help: in NLP, models like UDA (Unsupervised Data Augmentation) show improved text classification accuracy when unlabeled data scales from 10k to 1M examples, as the model learns richer linguistic patterns.

Practical considerations include computational cost and data quality. Larger datasets require more training time and memory, which can limit experimentation. Additionally, noisy or irrelevant unlabeled data (e.g., mislabeled images, off-topic text) can degrade performance. Techniques like data filtering, augmentation, or curriculum learning (prioritizing high-confidence samples) help mitigate this. For example, in self-training SSL, models iteratively generate pseudo-labels for unlabeled data, but incorrect pseudo-labels from low-quality data can propagate errors. Developers must weigh dataset size against these factors—sometimes, a smaller, cleaner dataset with strategic augmentation (e.g., rotation, cropping for images) outperforms a larger, noisier one. Tools like Active Learning can also optimize labeling efforts by prioritizing the most informative unlabeled samples.