Papers with successor model
Practical Obstacles to Deploying Active Learning (D19-1)
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| Challenge: | Active learning (AL) is a widely-used training strategy for maximizing predictive performance subject to a fixed annotation budget. |
| Approach: | They propose to use active learning to optimize predictive performance . they find that current approaches do not generalize reliably across models and tasks . |
| Outcome: | The proposed approach outperforms training on i.i.d. datasets on supervised learning tasks. |
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. |