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Humans are good at compositional zero-shot reasoning; someone who has neverseen a zebra before could nevertheless recognize one when we tell them it lookslike a horse with black and white stripes. Machine learning systems, on theother hand, usually leverage spurious correlations in the training data, andwhile such correlations can help recognize objects in context, they hurtgeneralization. To be able to deal with underspecified datasets while stillleveraging contextual clues during classification, we propose ProtoProp, anovel prototype propagation graph method. First we learn prototypicalrepresentations of objects (e.g., zebra) that are conditionally independentw.r.t. their attribute labels (e.g., stripes) and vice versa. Next we propagatethe independent prototypes through a compositional graph, to learncompositional prototypes of novel attribute-object combinations that reflectthe dependencies of the target distribution. The method does not rely on anyexternal data, such as class hierarchy graphs or pretrained word embeddings. Weevaluate our approach on AO-Clever, a synthetic and strongly visual datasetwith clean labels, and UT-Zappos, a noisy real-world dataset of fine-grainedshoe types. We show that in the generalized compositional zero-shot setting weoutperform state-of-the-art results, and through ablations we show theimportance of each part of the method and their contribution to the finalresults.

Independent Prototype Propagation for Zero-Shot Compositionality