A type of machine learning in which a model is trained using unlabeled data. Unlike supervised learning, where the model is trained on labeled data (i.e., data that is explicitly annotated with labels indicating the correct output), self-supervised learning does not require any labeled data.
Instead, the model is trained to make predictions about some aspect of the input data, such as predicting the next word in a sequence or filling in a missing segment of an image. The goal of self-supervised learning is to learn a useful representation of the input data that can be used for downstream tasks. By learning to predict certain aspects of the input data, the model can extract useful features and patterns from the data that can be used for other tasks.
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