Transferability and Hardness of Supervised Classification Tasks
international conference on computer vision, pp. 1395-1405, 2019.
We propose a novel approach for estimating the difficulty and transferability of supervised classification tasks. Unlike previous work, our approach is solution agnostic and does not require or assume trained models. Instead, we estimate these values using an information theoretic approach: treating training labels as random variables a...More
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