Self-Supervised Learning

A subfield of AI where models learn patterns from data to make predictions or decisions, improving with experience ra...

Learning where data arrives sequentially and the model updates continuously, often under changing distributions.

Reusing knowledge from a source task/domain to improve learning on a target task/domain, typically via pretrained mod...

Training one model on multiple tasks simultaneously to improve generalization through shared structure.

Methods that learn training procedures or initializations so models can adapt quickly to new tasks with little data.

A subfield of AI where models learn patterns from data to make predictions or decisions, improving with experience ra...

Learning a function from input-output pairs (labeled data), optimizing performance on predicting outputs for unseen i...

Learning structure from unlabeled data, such as discovering groups, compressing representations, or modeling data dis...

Training with a small labeled dataset plus a larger unlabeled dataset, leveraging assumptions like smoothness/cluster...