Jim Davis, Tong Liang
We lifted 7 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| ltong1130ztr/haframe | — | 7 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| DistanceDict | Ran | ltong1130ztr/haframe/HAFrame/solve_HAF.py pointer only (licence: NONE) · get_code("b2ebddc977309207") |
| cls_weights_matrix_factorization_solver | Ran | ltong1130ztr/haframe/HAFrame/solve_HAF.py pointer only (licence: NONE) · get_code("35485c4530562fe0") |
| find_max_separation_matrix_factorization_solver | Ran | ltong1130ztr/haframe/HAFrame/solve_HAF.py pointer only (licence: NONE) · get_code("88560c243b8f8496") |
| fixed_haf_cls_weights | Ran | ltong1130ztr/haframe/HAFrame/solve_HAF.py pointer only (licence: NONE) · get_code("480ca9eff5a52152") |
| load_distance_matrix | Ran | ltong1130ztr/haframe/HAFrame/solve_HAF.py pointer only (licence: NONE) · get_code("449bc1c4ca4fa6bb") |
| load_distances | Ran | ltong1130ztr/haframe/HAFrame/solve_HAF.py pointer only (licence: NONE) · get_code("4da9d5c7c6c3c9ce") |
| map_hdistance_to_cosine_similarity_exponential_decay | Ran | ltong1130ztr/haframe/HAFrame/solve_HAF.py pointer only (licence: NONE) · get_code("22db6d272cd3127f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
There is a recently discovered and intriguing phenomenon called Neural Collapse: at the terminal phase of training a deep neural network for classification, the within-class penultimate feature means and the associated classifier vectors of all flat classes collapse to the vertices of a simplex Equiangular Tight Frame (ETF). Recent work has tried to exploit this phenomenon by fixing the related classifier weights to a pre-computed ETF to induce neural collapse and maximize the separation of the learned features when training with imbalanced data. In this work, we propose to fix the linear classifier of a deep neural network to a Hierarchy-Aware Frame (HAFrame), instead of an ETF, and use a cosine similarity-based auxiliary loss to learn hierarchy-aware penultimate features that collapse to the HAFrame. We demonstrate that our approach reduces the mistake severity of the model's predictions while maintaining its top-1 accuracy on several datasets of varying scales with hierarchies of heights ranging from 3 to 12.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2303.05689")
get_code_for_paper("2303.05689")
have("2303.05689")
Connect an agent — have() is free.