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I want to reduce the effort required to label in-store product photos while ensuring the quality of the training data. Which method is most effective?
①Create a large number of manual labels using external crowdsourcing.
②Automatically assign labels using unsupervised clustering and train directly.
③In active learning, select only the most informative samples and have humans label them.
④Train using only a large number of generated synthetic images.
⑤Automatically optimize the labeling policy using reinforcement learning.