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USN: A Robust Imitation Learning Method against Diverse Action Noise.

, , and . J. Artif. Intell. Res., (2024)

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MCMC Based Generative Adversarial Networks for Handwritten Numeral Augmentation., , , and . CSPS, volume 463 of Lecture Notes in Electrical Engineering, page 2702-2710. Springer, (2017)Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework., , , , and . CoRR, (2019)Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels., , , , , , and . CoRR, (2018)USN: A Robust Imitation Learning Method against Diverse Action Noise., , and . J. Artif. Intell. Res., (2024)Co-sampling: Training Robust Networks for Extremely Noisy Supervision., , , , , , , and . CoRR, (2018)SIGUA: Forgetting May Make Learning with Noisy Labels More Robust., , , , , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 4006-4016. PMLR, (2020)How does Disagreement Help Generalization against Label Corruption?, , , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 7164-7173. PMLR, (2019)Intrinsic Reward Driven Imitation Learning via Generative Model., , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 10925-10935. PMLR, (2020)Co-teaching: Robust training of deep neural networks with extremely noisy labels., , , , , , , and . NeurIPS, page 8536-8546. (2018)