In daily life, we tend to present the front of our faces by staring squarely
at a facial recognition machine, instead of facing it sideways, in order to
increase the chance of being correctly recognised. Few-shot-learning (FSL)
classification is challenging in itself because a model has to identify images
that belong to classes previously unseen during training. Therefore, a warped
and non-typical query or support image during testing can make it even more
challenging for a model to predict correctly. In our work, to increase the
chance of correct prediction during testing, we aim to rectify the test input
of a trained FSL model by generating new samples of the tested classes through
image-to-image translation. An FSL model is usually trained on classes with
sufficient samples, and then tested on classes with few-shot samples. Our
proposed method first captures the style or shape of the test image, and then
identifies a suitable trained class sample. It then transfers the style or
shape of the test image to the train-class images for generation of more
test-class samples, before performing classification based on a set of
generated samples instead of just one sample. Our method has potential in
empowering a trained FSL model to score higher during the testing phase without
any extra training nor dataset. According to our experiments, by augmenting the
support set with just 1 additional generated sample, we can achieve around 2%
improvement for trained FSL models on datasets consisting of either animal
faces or traffic signs. By augmenting both the support set and the queries, we
can achieve even more performance improvement. Our Github Repository is
publicly available.

通过图像转换生成样本，并在生成的样本上进行分类，可以提高 few-shot-learning 模型在测试阶段的性能。

FSL 模型得分更高

FSL Model can Score Higher as It Is

Meta and transfer learning are two successful families of approaches to
few-shot learning. Despite highly related goals, state-of-the-art advances in
each family are measured largely in isolation of each other. As a result of
diverging evaluation norms, a direct or thorough comparison of different
approaches is challenging. To bridge this gap, we perform a cross-family study
of the best transfer and meta learners on both a large-scale meta-learning
benchmark (Meta-Dataset, MD), and a transfer learning benchmark (Visual Task
Adaptation Benchmark, VTAB). We find that, on average, large-scale transfer
methods (Big Transfer, BiT) outperform competing approaches on MD, even when
trained only on ImageNet. In contrast, meta-learning approaches struggle to
compete on VTAB when trained and validated on MD. However, BiT is not without
limitations, and pushing for scale does not improve performance on highly
out-of-distribution MD tasks. In performing this study, we reveal a number of
discrepancies in evaluation norms and study some of these in light of the
performance gap. We hope that this work facilitates sharing of insights from
each community, and accelerates progress on few-shot learning.

本研究对元学习和迁移学习两种方法在小样本学习领域的应用进行了交叉研究，以 Meta-Dataset 和 Visual Task Adaptation Benchmark 两个基准测试集进行了测试，发现大规模的迁移学习方法表现最佳。