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Continual Domain Adaptation

Learning process where a model progressively adapts to new domains while retaining its competencies on previously learned domains without access to the initial data.

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Weight Replication

Regularization technique that penalizes significant changes to network weights for parameters important to previous tasks.

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Continual Knowledge Distillation

Method where the current model learns to mimic the predictions of its previous version to preserve knowledge while adapting to the new domain.

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Continual Meta-Learning

Approach where the model learns to learn efficiently on new domains by developing fast and generalizable adaptation strategies.

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Dynamic Network Expansion

Architecture that selectively adds new capacities (neurons or layers) for new domains while preserving existing structures.

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Generative Replay

Technique using generative models to synthetically recreate data from previous domains and maintain performance during continual adaptation.

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Accumulated Domain Bias

Phenomenon where biases from previous domains accumulate and negatively affect performance on new domains in a continual adaptation scenario.

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Plasticity-Stability Dilemma

Fundamental constraint between a model's ability to adapt to new information (plasticity) and its ability to retain existing knowledge (stability).

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Continual Reinforcement Adaptation

Method where agents continuously learn to adapt to new environments using progressive feedback signals.

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Continual Task Selection

Mechanism enabling the model to identify and dynamically specialize in relevant domains among a continuous sequence of new domains.

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