  People easily recognize new visual categories that are new combinations of
known components. This compositional generalization capacity is critical for
learning in real-world domains like vision and language because the long tail
of new combinations dominates the distribution. Unfortunately, learning systems
struggle with compositional generalization because they often build on features
that are correlated with class labels even if they are not "essential" for the
class. This leads to consistent misclassification of samples from a new
distribution, like new combinations of known components.
  Here we describe an approach for compositional generalization that builds on
causal ideas. First, we describe compositional zero-shot learning from a causal
perspective, and propose to view zero-shot inference as finding "which
intervention caused the image?". Second, we present a causal-inspired embedding
model that learns disentangled representations of elementary components of
visual objects from correlated (confounded) training data. We evaluate this
approach on two datasets for predicting new combinations of attribute-object
pairs: A well-controlled synthesized images dataset and a real-world dataset
which consists of fine-grained types of shoes. We show improvements compared to
strong baselines.
