<ul><li>One of the major challenges in machine learning is maintaining the accuracy of the deployed model in a non-stationary environment.</li><li>The proposed method is a task-conditioned ensemble of expert models for continuous learning of the deployed model with new data.</li><li>The method uses in-domain models and task membership information to dynamically adapt the deployed model to new data while retaining accuracy on old data.</li><li>The experiments conducted on different setups demonstrate the benefits of the proposed method.</li></ul>

Task-conditioned Ensemble of Expert Models for Continuous Learning

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