Is self-supervised learning applicable to all types of data (images, text, audio)?

Direct Answer Yes, self-supervised learning (SSL) can be applied to images, text, and audio. SSL works by creating training tasks directly from the structure of the data itself, eliminating the need for manual labels. For example, a model might predict missing parts of an input (like a masked word in text) or learn relationships between data points (like identifying rotated images). While the specifics vary by data type, the core idea of leveraging inherent patterns works across modalities.

Application to Different Data Types For text, SSL is widely used. Models like BERT mask random words in sentences and train the model to predict them, learning contextual relationships. Similarly, GPT-style models predict the next word in a sequence. These tasks exploit the sequential and semantic structure of language. For images, SSL often involves tasks like predicting image rotations, reconstructing corrupted regions (inpainting), or contrasting augmented views of the same image (as in SimCLR). These methods rely on spatial and visual consistency. For audio, models like wav2vec hide segments of raw audio and train the model to predict the missing parts, capturing temporal and acoustic patterns. Each domain uses SSL to turn raw data into a supervisory signal without human annotation.

Practical Considerations While SSL is broadly applicable, its effectiveness depends on designing pretext tasks that align with the data’s inherent structure. For example, text’s sequential nature suits autoregressive or masking tasks, while images benefit from spatial transformations. Audio models must handle temporal continuity. Challenges like computational cost or data diversity can arise—training on high-resolution images requires significant resources, and audio models need large datasets to capture variability. However, frameworks like contrastive learning or transformer architectures have shown adaptability across domains. Developers can implement SSL using libraries like Hugging Face (text), PyTorch Lightning (images), or SpeechBrain (audio), tailoring approaches to their specific data type.