A new research paper introduces the concept of "loss shift" as a distinct challenge in transfer learning, separate from distribution shift. The paper formalizes this by using Bayes quotients to order losses by refinement, identifying when a representation suitable for a coarser loss is insufficient for a strictly finer target loss. Experiments across various settings demonstrate that classification-equivalent representations can yield different optimal performance under a fixed data distribution when loss functions vary. AI
RANK_REASON The cluster contains a new academic paper on a machine learning topic. [lever_c_demoted from research: ic=1 ai=1.0]
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