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Discovering evolutionary traits that are heritable across species on the treeof life (also referred to as a phylogenetic tree) is of great interest tobiologists to understand how organisms diversify and evolve. However, themeasurement of traits is often a subjective and labor-intensive process, makingtrait discovery a highly label-scarce problem. We present a novel approach fordiscovering evolutionary traits directly from images without relying on traitlabels. Our proposed approach, Phylo-NN, encodes the image of an organism intoa sequence of quantized feature vectors -- or codes -- where different segmentsof the sequence capture evolutionary signals at varying ancestry levels in thephylogeny. We demonstrate the effectiveness of our approach in producingbiologically meaningful results in a number of downstream tasks includingspecies image generation and species-to-species image translation, using fishspecies as a target example.

Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks