2 citations · 6 across the 13 of their papers we have counts for
4 papers · 1 filter
Improving Bias Mitigation through Bias Experts in Natural Language Understanding
Eojin Jeon, Mingyu Lee, Juhyeong Park +3
Biases in the dataset often enable the model to achieve high performance on in-distribution data, while poorly performing on out-of-distribution data. To mitigate the detrimental e…
Learning From Drift: Federated Learning on Non-IID Data via Drift Regularization
Yeachan Kim, Bonggun Shin
Federated learning algorithms perform reasonably well on independent and identically distributed (IID) data. They, on the other hand, suffer greatly from heterogeneous environments…
Dynamic Structure Pruning for Compressing CNNs
Jun-Hyung Park, Yeachan Kim, Junho Kim +2
Structure pruning is an effective method to compress and accelerate neural networks. While filter and channel pruning are preferable to other structure pruning methods in terms of…
Phase-shifted Adversarial Training
Yeachan Kim, Seongyeon Kim, Ihyeok Seo +1
Adversarial training has been considered an imperative component for safely deploying neural network-based applications to the real world. To achieve stronger robustness, existing…