  We present an image preprocessing technique capable of improving the
performance of few-shot classifiers on abstract visual reasoning tasks. Many
visual reasoning tasks with abstract features are easy for humans to learn with
few examples but very difficult for computer vision approaches with the same
number of samples, despite the ability for deep learning models to learn
abstract features. Same-different (SD) problems represent a type of visual
reasoning task requiring knowledge of pattern repetition within individual
images, and modern computer vision approaches have largely faltered on these
classification problems, even when provided with vast amounts of training data.
We propose a simple method for solving these problems based on the insight that
removing peaks from the amplitude spectrum of an image is capable of
emphasizing the unique parts of the image. When combined with several
classifiers, our method performs well on the SD SVRT tasks with few-shot
learning, improving upon the best comparable results on all tasks, with average
absolute accuracy increases nearly 40% for some classifiers. In particular, we
find that combining Relational Networks with this image preprocessing approach
improves their performance from chance-level to over 90% accuracy on several SD
tasks.
