Discovering evolutionary traits that are heritable across species on the tree
of life (also referred to as a phylogenetic tree) is of great interest to
biologists to understand how organisms diversify and evolve. However, the
measurement of traits is often a subjective and labor-intensive process, making
trait discovery a highly label-scarce problem. We present a novel approach for
discovering evolutionary traits directly from images without relying on trait
labels. Our proposed approach, Phylo-NN, encodes the image of an organism into
a sequence of quantized feature vectors -- or codes -- where different segments
of the sequence capture evolutionary signals at varying ancestry levels in the
phylogeny. We demonstrate the effectiveness of our approach in producing
biologically meaningful results in a number of downstream tasks including
species image generation and species-to-species image translation, using fish
species as a target example.

Phylo-NN 是一种无需标签，直接从昆虫图片中提取进化特征序列的方法，对鱼类进行了演示，证明了其用于物种图像生成和物种 - 物种图像转换的有效性。