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doi: 10.1002/eji.200838318 88 ClambeyETMcNameeENWestrichJAGloverLECampbellELJedlickaPet al

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To avoid trivial matches, we mask out knowledge embeddings that come from another sample but share the same class as sample b : Thus, the loss for the image-to-knowledge direction is: Similarly, for the knowledge-to-image direction: Both directions are averaged across the batch: The final global contrastive loss is computed by averaging across both directions: Model inference During inference, we process a query image through both arms to generate predictions

Thus, there remains significant areas to explore to understand the role of glucagon

doi:10.1097/MCP.0000000000000733