Papers by Yi-Fan Li
SetConv: A New Approach for Learning from Imbalanced Data (2020.emnlp-main)
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| Challenge: | Existing methods for classification are biased towards the majority class when the Imbalance Ratio (IR) is high. |
| Approach: | They propose a set convolution operation and an episodic training strategy to extract a single representative for each class, so that classifiers can later be trained on a balanced class distribution. |
| Outcome: | The proposed algorithm is permutation-invariant despite the order of inputs and shows superiority on multiple large-scale benchmark text datasets. |