Jul 13, 2021 · Our results show that unnecessary input dimensions that are task-unrelated substantially degrade data efficiency.
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Sep 28, 2020 · Abstract: Input dimensions are unnecessary for a given task when the target function can be expressed without such dimensions.
Jul 15, 2021 · Datasets often contain input dimensions that are unnecessary to predict the output label, e.g., background in object recognition, ...
Input dimensions are unnecessary for a given task when the target function can be expressed without such dimensions. Object's background in image ...
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Datasets often contain input dimensions that are unnecessary to predict the output label, e.g. background in object recognition, which lead to more ...
Our results show that unnecessary input dimensions that are task-unrelated substantially degrade data efficiency.
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Our results show that unnecessary input dimensions that are task-unrelated substantially degrade data efficiency. This highlights the need for mechanisms that ...
Missing: Foes | Show results with:Foes
Jan 10, 2023 · The larger the input size, the more parameters the network has to learn, which can lead to overfitting if the dataset is not large enough.
In this letter, we investigate the impact of unnecessary input dimensions on a central issue of DNNs: their data efficiency, ie., the amount of examples needed ...
Jan 20, 2023 · Capacity to learn: Increasing the size of a neural network generally increases its capacity to learn and represent complex patterns in the data.