Papers with acquisition model
Towards Computationally Feasible Deep Active Learning (2022.findings-naacl)
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Akim Tsvigun, Artem Shelmanov, Gleb Kuzmin, Leonid Sanochkin, Daniil Larionov, Gleb Gusev, Manvel Avetisian, Leonid Zhukov
| Challenge: | Active learning (AL) is a technique for reducing the amount of annotation required for training machine learning models. |
| Approach: | They propose two techniques that reduce the amount of time required for AL . they use pseudo-labeling and distilled models to train a successor model . |
| Outcome: | The proposed algorithm reduces the time and computational overhead required to train an acquisition model and estimate uncertainty on instances in the unlabeled pool. |
Efficient Active Learning with Adapters (2024.findings-emnlp)
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| Challenge: | Existing studies show that distilled versions of pretrained models are not always available. |
| Approach: | They propose to use distilled versions of successor models as acquisition models to reduce the training cost of the model. |
| Outcome: | The proposed approach reduces the training cost of the model and does not cause the acquisition-successor mismatch (ASM) problem. |