Autoencoders play a key role in self-supervised learning by enabling models to learn meaningful representations of data without relying on labeled examples. They achieve this through a two-step process: compressing input data into a lower-dimensional latent space (encoding) and reconstructing the original input from this compressed representation (decoding). In self-supervised setups, the model is trained to solve a pretext task—like reconstructing corrupted or masked input—which forces it to capture essential patterns in the data. This learned representation can then be reused for downstream tasks such as classification or clustering.
For example, a denoising autoencoder is trained to remove noise from corrupted input data. By feeding the model noisy images and requiring it to output clean versions, the encoder learns to identify robust features like edges or textures. Similarly, in natural language processing, masked autoencoders (e.g., BERT-style models) predict missing words in a sentence. These tasks don’t require manual labels—the “label” is the original uncorrupted data. The encoder’s output becomes a reusable feature vector that captures semantic relationships in the data, which can be fine-tuned for specific applications like sentiment analysis or object detection.
In practice, autoencoders are often used as a pretraining step. For instance, a vision model might first be trained as an autoencoder on unlabeled images to learn general features like shapes or gradients. The encoder can then be attached to a smaller task-specific head (e.g., a classifier) and fine-tuned with limited labeled data. This approach is efficient because the bulk of the model’s capacity is already tuned to the data domain. However, care must be taken to avoid trivial solutions, such as the model learning to copy input details without generalization. Techniques like adding noise, sparsity constraints, or variational components (as in VAEs) help ensure the latent space captures useful structure. Autoencoders thus provide a flexible framework for self-supervised representation learning across modalities.