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What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits

3 Sep 2024arXiv:2409.02335archive 2025-07-28

Harish Babu Manogaran, M. Maruf, Arka Daw, Kazi Sajeed Mehrab, Caleb Patrick Charpentier, Josef C. Uyeda, Wasila Dahdul, Matthew J Thompson, Elizabeth G Campolongo, Kaiya L Provost, Paula M. Mabee, Hilmar Lapp, Anuj Karpatne

A grand challenge in biology is to discover evolutionary traits - features of organisms common to a group of species with a shared ancestor in the tree of life (also referred to as phylogenetic tree). With the growing availability of image repositories in biology, there is a tremendous opportunity to discover evolutionary traits directly from images in the form of a hierarchy of prototypes. However, current prototype-based methods are mostly designed to operate over a flat structure of classes and face several challenges in discovering hierarchical prototypes, including the issue of learning over-specific features at internal nodes. To overcome these challenges, we introduce the framework of Hierarchy aligned Commonality through Prototypical Networks (HComP-Net). We empirically show that HComP-Net learns prototypes that are accurate, semantically consistent, and generalizable to unseen species in comparison to baselines on birds, butterflies, and fishes datasets. The code and datasets are available at https://github.com/Imageomics/HComPNet.

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alter_name_for_cub_phylogeny Imageomics/HComPNet/preprocess_data/prepare_cub190.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 589a58686081bfab · report
apply_segmentation_mask_with_imagenet_mean Imageomics/HComPNet/preprocess_data/prepare_cub190.py official repository ran · honoured contract MIT (permissive) · 133c7b2a2ef12d5c · report
exclude_bias_and_batchnorm Imageomics/HComPNet/util/args.py official repository ran · our draft was wrong MIT (permissive) · 752d0617b8364e73 · report
get_patch_size imageomics/hcompnet/util/func.py official repository ran MIT (permissive) · fc27da95703ce673 · report
is_bias_or_batchnorm Imageomics/HComPNet/util/args.py official repository ran · violated contract MIT (permissive) · f0fa33b411bf906e · report
make_squared imageomics/hcompnet/preprocess_data/prepare_cub190.py official repository ran MIT (permissive) · 3f4ca34ecc5188e9 · report
replace_convlayers_convnext imageomics/hcompnet/features/convnext_features.py official repository ran MIT (permissive) · a758c95ddd9cf6e6 · report
split_value imageomics/hcompnet/util/node.py official repository ran fingerprinted MIT (permissive) · 8a56e2bc50b4bd55 · report
convnext_tiny_13_features imageomics/hcompnet/features/convnext_features.py official repository unverified MIT (permissive) · 094b66474a792977 · report
convnext_tiny_26_features imageomics/hcompnet/features/convnext_features.py official repository unverified MIT (permissive) · c3c86ab4d3337a4f · report
create_filtered_dataloader imageomics/hcompnet/util/data.py official repository unverified MIT (permissive) · cda3521af97352b1 · report
get_data imageomics/hcompnet/util/data.py official repository unverified MIT (permissive) · f07e5e822b7d8619 · report
get_optimizer_nn Imageomics/HComPNet/util/args.py official repository unverified MIT (permissive) · 77b218509c30e14c · report
init_weights_xavier imageomics/hcompnet/util/func.py official repository unverified MIT (permissive) · 4195aead1609eeb2 · report
unshuffle_dataloader imageomics/hcompnet/util/data.py official repository unverified MIT (permissive) · 4d3d935f60f0e4a1 · report

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