sample selection

DualGraph: A Graph-Based Method for Reasoning About Label Noise CVPR 2021 FINE Samples for Learning with Noisy Labels NeurIPS 2021 GOAL: Gist-set Online Active Learning for Efficient Chest X-ray Image Annotation MIDL 2021 Jo-SRC: A Contrastive Approach for Combating Noisy Labels CVPR 2021 Learning to Selectively Learn for Weakly-supervised Paraphrase Generation EMNLP 2021 Minimizing Annotation Effort via Max-Volume Spectral Sampling EMNLP 2021 Model Uncertainty Guides Visual Object Tracking AAAI 2021 Noise against noise: stochastic label noise helps combat inherent label noise ICLR 2021 One for More: Selecting Generalizable Samples for Generalizable ReID Model AAAI 2021 QActor: Active Learning on Noisy Labels ACML 2021 Sample Selection for Fair and Robust Training NeurIPS 2021 Sample Selection for Universal Domain Adaptation AAAI 2021 Selective Knowledge Distillation for Neural Machine Translation ACL 2021 To Share or not to Share: Predicting Sets of Sources for Model Transfer Learning EMNLP 2021 Unsupervised Active Learning via Subspace Learning AAAI 2021 UXLA: A Robust Unsupervised Data Augmentation Framework for Zero-Resource Cross-Lingual NLP ACL 2021 Asking the Right Questions to the Right Users: Active Learning with Imperfect Oracles AAAI 2020 Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection CVPR 2020 Choosing the Sample with Lowest Loss makes SGD Robust AISTATS 2020 Curriculum Loss: Robust Learning and Generalization against Label Corruption ICLR 2020 On Deep Unsupervised Active Learning IJCAI 2020 Searching to Exploit Memorization Effect in Learning with Noisy Labels ICML 2020 Active Generative Adversarial Network for Image Classification AAAI 2019 Gradient based sample selection for online continual learning NeurIPS 2019 Libra R-CNN: Towards Balanced Learning for Object Detection CVPR 2019