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DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture., , , , , , and . Trans. Mach. Learn. Res., (2022)DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation., , , , , , , and . CoRR, (2020)VEGA: Towards an End-to-End Configurable AutoML Pipeline., , , , , , , , , and 14 other author(s). CoRR, (2020)Generative Negative Text Replay for Continual Vision-Language Pretraining., , , , , , and . ECCV (36), volume 13696 of Lecture Notes in Computer Science, page 22-38. Springer, (2022)Generalizing Few-Shot NAS with Gradient Matching., , , , , and . ICLR, OpenReview.net, (2022)Memory Replay with Data Compression for Continual Learning., , , , , , , , , and . ICLR, OpenReview.net, (2022)MetaAugment: Sample-Aware Data Augmentation Policy Learning., , , , , , and . AAAI, page 11097-11105. AAAI Press, (2021)MetaAugment: Sample-Aware Data Augmentation Policy Learning., , , , , , and . CoRR, (2020)Adversarial Robustness for Unsupervised Domain Adaptation., , , , , , and . ICCV, page 8548-8557. IEEE, (2021)ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-Supervised Continual Learning., , , , , and . CVPR, page 5383-5392. Computer Vision Foundation / IEEE, (2021)