  Recent studies on few-shot classification using transfer learning pose
challenges to the effectiveness and efficiency of episodic meta-learning
algorithms. Transfer learning approaches are a natural alternative, but they
are restricted to few-shot classification. Moreover, little attention has been
on the development of probabilistic models with well-calibrated uncertainty
from few-shot samples, except for some Bayesian episodic learning algorithms.
To tackle the aforementioned issues, we propose a new transfer learning method
to obtain accurate and reliable models for few-shot regression and
classification. The resulting method does not require episodic meta-learning
and is called meta-free representation learning (MFRL). MFRL first finds
low-rank representation generalizing well on meta-test tasks. Given the learned
representation, probabilistic linear models are fine-tuned with few-shot
samples to obtain models with well-calibrated uncertainty. The proposed method
not only achieves the highest accuracy on a wide range of few-shot learning
benchmark datasets but also correctly quantifies the prediction uncertainty. In
addition, weight averaging and temperature scaling are effective in improving
the accuracy and reliability of few-shot learning in existing meta-learning
algorithms with a wide range of learning paradigms and model architectures.
