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Domain-adaptive transformer

A Domain-Adaptive Transformer (DAT) is a Transformer-based model — such as BERT or ViT — extended with an explicit domain-alignment objective so that learned representations transfer well from a labeled source domain to a different, often unlabeled, target domain. The approach combines the powerful representation capacity of Transformers with domain adaptation techniques such as adversarial training or contrastive alignment to minimise domain shift.

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Domain-Adaptive Transformer (DAT)

Rekodi ya mbinu ya kiajenda · ml-model / deep-learning

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Same method familyTransfer Learningmachine-suggested · Relational suggestion, not evidence.Same method familyVision Transformermachine-suggested · Relational suggestion, not evidence.