Deep Neural Networks Can Learn Generalizable Same-Different Visual
  Relations
Alexa R. Tartaglini Sheridan Feucht Michael A. Lepori Wai Keen Vong Charles Lovering Brenden M. Lake Ellie Pavlick
Although deep neural networks can achieve human-level performance on many
object recognition benchmarks, prior work suggests that these same models fail
to learn simple abstract relations, such as determining whether two objects are
the same or different. Much of this prior work focuses on training
convolutional neural networks to classify images of two same or two different
abstract shapes, testing generalization on within-distribution stimuli. In this
article, we comprehensively study whether deep neural networks can acquire and
generalize same-different relations both within and out-of-distribution using a
variety of architectures, forms of pretraining, and fine-tuning datasets. We
find that certain pretrained transformers can learn a same-different relation
that generalizes with near perfect accuracy to out-of-distribution stimuli.
Furthermore, we find that fine-tuning on abstract shapes that lack texture or
color provides the strongest out-of-distribution generalization. Our results
suggest that, with the right approach, deep neural networks can learn
generalizable same-different visual relations.