<ul><li>The paper discusses the concept of open world learning in the context of machine learning.</li><li>Traditional machine learning performs well on static benchmarks, but struggles with real-world dynamics.</li><li>The researchers propose leveraging the batch normalization layer in neural networks to achieve open world learning.</li><li>By using the tracked statistics of batch normalization, the models can become more robust and adaptable.</li></ul>

BOWL: A Deceptively Simple Open World Learner

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