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reduces reliance on manual labeling while helping maintain long-term model performance.

Types

Autoassociative self-supervised learning

Autoassociative self-supervised learning is a specific category of self-supervised learning where a neural network is trained to reproduce or reconstruct its own input data. In other words, the model is tasked with learning a representation of the data that captures its essential features or structure, allowing it to regenerate the original input.

The term "autoassociative"

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