Results for "continual adaptation"

Loss of old knowledge when learning new tasks.

PEFT method injecting trainable low-rank matrices into layers, enabling efficient fine-tuning.

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

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 mismatch between training and deployment data distributions that can degrade model performance.

Randomly zeroing activations during training to reduce co-adaptation and overfitting.

Techniques that fine-tune small additional components rather than all weights to reduce compute and storage.

Maintaining alignment under new conditions.

Train/test environment mismatch.

Performance drop when moving from simulation to reality.

Combining simulation and real-world data.

Learning by minimizing prediction error.

Software regulated as a medical device.

Differences between training and deployed patient populations.

Rate at which AI capabilities improve.