We propose the Hardness Aware Reweighting (HAR) framework, which circumvents this issue by increasing the loss contribution of hard examples from both the ...
This presents a need for developing a hardness-aware, instance-specific reweighting strategy suitable for training deep neural networks. We propose the Hardness ...
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Har: Hardness aware reweighting for imbalanced datasets. R Duggal, S Freitas, S Dhamnani, DH Chau, J Sun. 2021 IEEE International Conference on Big Data (Big ...
Jun 3, 2022 · If there's unbalanced datasets, what's the way to proceed? The canonical answer seems to be over/under sampling and class reweighting (is ...
Missing: HAR: Hardness Aware
Sep 30, 2022 · HAR: Hardness Aware Reweighting for Imbal- anced Datasets. In 2021 IEEE International Conference on. Big Data (Big Data), 735–745. IEEE ...
Data in the real world is commonly imbalanced across classes. Training neural networks on imbalanced datasets often leads to poor performance on rare classes.
HAR: Hardness Aware Reweighting for Imbalanced Datasets. 2021, arXiv, Feature Generation for Long-tail Classification. 2021, arXiv, Label-Aware Distribution ...
Har: Hardness aware reweighting for imbalanced datasets. R Duggal, S Freitas, S Dhamnani, DH Chau, J Sun. 2021 IEEE International Conference on Big Data (Big ...
I'm a Senior Applied Scientist at Microsoft working at the intersection of applied and theoretical machine learning, with a focus on graph mining and deep ...