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- W3035989815 abstract "Deep neural networks have shown excellent performances in many real-world applications. Unfortunately, they may show Hans-like behavior---making use of confounding factors within datasets---to achieve high performance. In this work, we introduce the novel setting of explanatory interactive learning (XIL) and illustrate its benefits on a plant phenotyping research task. XIL adds the scientist into the training loop such that she interactively revises the original model via providing feedback on its explanations. Our experimental results demonstrate that XIL can help avoiding Clever Hans moments in machine and encourages (or discourages, if appropriate) trust into the underlying model." @default.
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- W3035989815 date "2020-01-15" @default.
- W3035989815 modified "2023-09-27" @default.
- W3035989815 title "Making deep neural networks right for the right scientific reasons by interacting with their explanations" @default.
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