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Limitations of End-to-End Deep Learning
- Large amount of data is necessary: incorporating some prior knowledge into the training improves performance and is considered a key element. For end-to-end learning more training examples must be provided because we don't incorporate this prior knowledge.
- Difficult to improve or modify the system: if some structural change must be applied, the old model is of no use and the whole model has to be replaced and trained all over again.
- Potentially useful hand-designed modules cannot be used: many hand-designed modules hold a lot of human knowledge and are efficient in solving some tasks, especially when the available data is limited.
- Difficult to validate: due to the complex architecture, the potential number of input/output pairs can be big enough to make high level validation impossible.
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Updated 2021-04-07
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