Selvsuperviserede

Selvsuperviserede, often translated as self-supervised learning, is a type of machine learning where algorithms learn from data that has not been explicitly labeled. Instead of relying on human-provided labels, self-supervised methods generate their own supervisory signals from the inherent structure of the input data. This is typically achieved by creating "pretext tasks" where a portion of the data is masked or altered, and the model is trained to predict the missing or original information.

For example, in natural language processing, a self-supervised model might be trained to predict a masked word in a sentence based on the surrounding words. In computer vision, a model could be trained to predict the relative position of image patches or to reconstruct a corrupted image. By solving these pretext tasks, the model learns valuable representations of the data that can then be transferred to downstream tasks, such as classification or translation, often with significantly less labeled data required for fine-tuning. This approach is particularly useful in domains where obtaining large amounts of labeled data is expensive or time-consuming. Self-supervised learning has demonstrated impressive results and is a key enabler of large language models and advanced computer vision systems.