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- W4308236844 abstract "This paper considers deep out-of-distribution active learning. In practice, fully trained neural networks interact randomly with out-of-distribution (OOD) inputs and map aberrant samples randomly within the model representation space. Since data representations are direct manifestations of the training distribution, the data selection process plays a crucial role in outlier robustness. For paradigms such as active learning, this is especially challenging since protocols must not only improve performance on the training distribution most effectively but further render a robust representation space. However, existing strategies directly base the data selection on the data representation of the unlabeled data which is random for OOD samples by definition. For this purpose, we introduce forgetful active learning with switch events (FALSE) - a novel active learning protocol for out-of-distribution active learning. Instead of defining sample importance on the data representation directly, we formulate informativeness with learning difficulty during training. Specifically, we approximate how often the network forgets unlabeled samples and query the most forgotten samples for annotation. We report up to 4.5% accuracy improvements in over 270 experiments, including four commonly used protocols, two OOD benchmarks, one in-distribution benchmark, and three different architectures." @default.
- W4308236844 created "2022-11-09" @default.
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- W4308236844 date "2022-10-16" @default.
- W4308236844 modified "2023-09-30" @default.
- W4308236844 title "Forgetful Active Learning with Switch Events: Efficient Sampling for Out-of-Distribution Data" @default.
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- W4308236844 doi "https://doi.org/10.1109/icip46576.2022.9897514" @default.
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