FSL Model can Score Higher as It Is
Yunwei Bai Ying Kiat Tan Tsuhan Chen
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.