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If your goal is to predict given an image multiple labels (each of them can be binary or multi-class) you could consider two strategies: Create for each classification task a separate model, which predicts solves only one problem Create a single model with multiple heads The first option seems to be more straightforward, but it would most likely need to ...


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This depends on the behaviour you want. If the ambiguous sample's ground truth is classified by a range of people, your network will get an average* based on that group. If it's only by one person, your network will be biased to how that one person classifies these samples. Alternatively, depending on your loss function, you could train the network to ...


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