Papers with meta-learning module
DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation (2023.acl-long)
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| Challenge: | Existing approaches to domain adaptation only use reliable pseudo instances, i.e., pseudo instances with high prediction confidence, to retrain the model. |
| Approach: | They propose a domain adversarial learning enhanced self-training framework that uses meta-learning to estimate the importance of each pseudo instance and a meta constructor to construct the meta-validation set. |
| Outcome: | The proposed framework reduces label noise and preserves hard examples while maintaining accuracy. |
MetaReVision: Meta-Learning with Retrieval for Visually Grounded Compositional Concept Acquisition (2023.findings-emnlp)
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| Challenge: | Existing methods to learn compositional concepts from previous experience are based on systematic generalization, productivity and substitutivity. |
| Approach: | They propose a retrieval-enhanced meta-learning model to solve the visually grounded compositional concept learning problem by meta-training retrieved primitive concepts from episodes constructed by the retriever. |
| Outcome: | The proposed model outperforms other baselines and the retrieval module plays an important role in this compositional learning process. |