Can self-supervised learning be used on noisy data?

Yes, self-supervised learning (SSL) can effectively handle noisy data, though its success depends on the type and severity of the noise and how the method is designed. SSL works by creating training signals directly from the input data, bypassing the need for manually labeled datasets. This approach inherently builds robustness to certain types of noise, as the model learns to identify underlying patterns rather than relying on precise annotations. For example, in vision tasks, SSL methods like contrastive learning train models to recognize similarities between augmented versions of the same image (e.g., cropped, rotated, or color-distorted), which mimics noisy variations naturally. The model learns to focus on invariant features, filtering out irrelevant noise in the process.

A key advantage of SSL is its ability to use large amounts of unlabeled data—including noisy samples—to pre-train models, which can later be fine-tuned on smaller, cleaner datasets. For instance, in natural language processing (NLP), models like BERT are trained using masked language modeling, where random words in a sentence are replaced or obscured. Even if the original text contains typos or grammatical errors, the model learns to predict missing tokens based on context, effectively ignoring minor noise. Similarly, in audio processing, SSL can handle background noise by training on raw waveforms or spectrograms with random masking, forcing the model to focus on speech patterns rather than transient artifacts.

However, SSL isn’t immune to all forms of noise. If the noise is systematic (e.g., sensor errors in time-series data or adversarial perturbations), it can corrupt the learned representations. Developers should preprocess data to remove obvious outliers or employ noise-robust SSL techniques. For example, in computer vision, combining SSL with denoising autoencoders can help reconstruct clean images from noisy inputs. Additionally, data augmentations that mimic real-world noise (e.g., adding Gaussian noise to images) during pre-training can improve robustness. While SSL isn’t a magic solution, its flexibility in learning from raw data makes it a practical choice for noisy datasets, provided the noise isn’t structural or adversarial in nature.