A neural network is trained in a self-supervised manner by using the data itself to create pseudo-labels or tasks for learning. Instead of relying on explicitly labeled datasets, self-supervised learning leverages the structure and features inherent in the data to derive labels. For instance, given a set of images, a self-supervised learning approach might involve training a network to predict the missing part of an image or to determine whether two augmented versions of the same image are from the same original source. This way, the model learns to identify meaningful patterns in the data without the need for manual annotations.
One common tactic in self-supervised learning involves creating surrogate tasks. For example, in the field of computer vision, one may use a technique called "contrastive learning." In this approach, the model is trained to differentiate between similar and dissimilar pairs of images. By augmenting images — such as cropping, rotating, or altering colors — and then pairing the same modified image, the neural network learns to encode and distinguish between variations, ultimately improving its understanding of the underlying data distribution without needing label information. This training occurs on a vast amount of unlabeled data, allowing the network to develop robust features that can later be fine-tuned for specific tasks.
In natural language processing, a popular self-supervised task is predicting the next word in a sentence or filling in a missing word. For instance, models like BERT and GPT utilize vast text corpora to learn the contextual relationships between words and phrases. During training, they randomly mask words in a sentence and task the model with predicting those masked words based on surrounding context. This method not only provides a rich source of data for training but also enables the model to capture semantic relationships and linguistic structures. Once trained, the resulting neural network can then be employed for various downstream tasks, such as sentiment analysis or machine translation, thereby demonstrating the effectiveness of self-supervised learning.