Solving Visual Analogies Using Neural Algorithmic Reasoning

• URL: http://arxiv.org/abs/2111.10361v1
• Date: Fri, 19 Nov 2021 18:48:16 GMT
• Title: Solving Visual Analogies Using Neural Algorithmic Reasoning
• Authors: Atharv Sonwane, Gautam Shroff, Lovekesh Vig, Ashwin Srinivasan, Tirtharaj Dash
• Abstract summary: We search for a sequence of elementary neural network transformations that manipulate distributed representations derived from a symbolic space. We evaluate the extent to which our neural reasoning' approach generalizes for images with unseen shapes and positions.
• Score: 22.384921045720752
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider a class of visual analogical reasoning problems that involve discovering the sequence of transformations by which pairs of input/output images are related, so as to analogously transform future inputs. This program synthesis task can be easily solved via symbolic search. Using a variation of the `neural analogical reasoning' approach of (Velickovic and Blundell 2021), we instead search for a sequence of elementary neural network transformations that manipulate distributed representations derived from a symbolic space, to which input images are directly encoded. We evaluate the extent to which our `neural reasoning' approach generalizes for images with unseen shapes and positions.

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