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Found 4 papers, 2 papers with code
Quadapter: Adapter for GPT-2 Quantization
In that case, it is observed that quantization-aware training overfits the model to the fine-tuning data.
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks
While tasks could come with varying the number of instances and classes in realistic settings, the existing meta-learning approaches for few-shot classification assume that the number of instances per task and class is fixed.
MxML: Mixture of Meta-Learners for Few-Shot Classification
A meta-model is trained on a distribution of similar tasks such that it learns an algorithm that can quickly adapt to a novel task with only a handful of labeled examples.
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class.
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