%0 Generic %1 cao2020heteroskedastic %A Cao, Kaidi %A Chen, Yining %A Lu, Junwei %A Arechiga, Nikos %A Gaidon, Adrien %A Ma, Tengyu %D 2020 %K 2020 dataset deep-learning %T Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization %U http://arxiv.org/abs/2006.15766 %X Real-world large-scale datasets are heteroskedastic and imbalanced -- labels have varying levels of uncertainty and label distributions are long-tailed. Heteroskedasticity and imbalance challenge deep learning algorithms due to the difficulty of distinguishing among mislabeled, ambiguous, and rare examples. Addressing heteroskedasticity and imbalance simultaneously is under-explored. We propose a data-dependent regularization technique for heteroskedastic datasets that regularizes different regions of the input space differently. Inspired by the theoretical derivation of the optimal regularization strength in a one-dimensional nonparametric classification setting, our approach adaptively regularizes the data points in higher-uncertainty, lower-density regions more heavily. We test our method on several benchmark tasks, including a real-world heteroskedastic and imbalanced dataset, WebVision. Our experiments corroborate our theory and demonstrate a significant improvement over other methods in noise-robust deep learning.

@misc{cao2020heteroskedastic, abstract = {Real-world large-scale datasets are heteroskedastic and imbalanced -- labels have varying levels of uncertainty and label distributions are long-tailed. Heteroskedasticity and imbalance challenge deep learning algorithms due to the difficulty of distinguishing among mislabeled, ambiguous, and rare examples. Addressing heteroskedasticity and imbalance simultaneously is under-explored. We propose a data-dependent regularization technique for heteroskedastic datasets that regularizes different regions of the input space differently. Inspired by the theoretical derivation of the optimal regularization strength in a one-dimensional nonparametric classification setting, our approach adaptively regularizes the data points in higher-uncertainty, lower-density regions more heavily. We test our method on several benchmark tasks, including a real-world heteroskedastic and imbalanced dataset, WebVision. Our experiments corroborate our theory and demonstrate a significant improvement over other methods in noise-robust deep learning.}, added-at = {2020-07-06T12:17:42.000+0200}, author = {Cao, Kaidi and Chen, Yining and Lu, Junwei and Arechiga, Nikos and Gaidon, Adrien and Ma, Tengyu}, biburl = {https://www.bibsonomy.org/bibtex/2c5d4f8f0ca2b46a38d1695bf8c0ef946/analyst}, description = {[2006.15766] Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization}, interhash = {dee5a283ee5644563599e1d4ba51b0a7}, intrahash = {c5d4f8f0ca2b46a38d1695bf8c0ef946}, keywords = {2020 dataset deep-learning}, note = {cite arxiv:2006.15766}, timestamp = {2020-07-06T12:17:42.000+0200}, title = {Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization}, url = {http://arxiv.org/abs/2006.15766}, year = 2020 }