Open-sampling: Exploring out-of-distribution data for re-balancing long-tailed datasets
Deep neural networks usually perform poorly when the training dataset suffers from extreme
class imbalance. Recent studies found that directly training with out-of-distribution data (ie …
class imbalance. Recent studies found that directly training with out-of-distribution data (ie …
[PDF][PDF] Open-Sampling: Exploring Out-of-Distribution Data for Re-balancing Long-tailed Datasets
H Wei, L Tao, R Xie, L Feng, B An - personal.ntu.edu.sg
Deep neural networks usually perform poorly when the training dataset suffers from extreme
class imbalance. Recent studies found that directly training with out-of-distribution data (ie …
class imbalance. Recent studies found that directly training with out-of-distribution data (ie …
Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets
H Wei, L Tao, R Xie, L Feng, B An - arXiv e-prints, 2022 - ui.adsabs.harvard.edu
Deep neural networks usually perform poorly when the training dataset suffers from extreme
class imbalance. Recent studies found that directly training with out-of-distribution data (ie …
class imbalance. Recent studies found that directly training with out-of-distribution data (ie …
Open-Sampling: Exploring Out-of-Distribution data for Re-balancing Long-tailed datasets
H Wei, L Tao, R Xie, L Feng, B An - arXiv preprint arXiv:2206.08802, 2022 - arxiv.org
Deep neural networks usually perform poorly when the training dataset suffers from extreme
class imbalance. Recent studies found that directly training with out-of-distribution data (ie …
class imbalance. Recent studies found that directly training with out-of-distribution data (ie …