Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
P$^2$OT: Progressive Partial Optimal Transport for Deep Imbalanced Clustering
Authors: Chuyu Zhang, Hui Ren, Xuming He
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on various datasets, including a humancurated long-tailed CIFAR100, challenging Image Net-R, and large-scale subsets of fine-grained i Naturalist2018 datasets, demonstrate the superiority of our method. |
| Researcher Affiliation | Academia | Chuyu Zhang1,2, Hui Ren1, Xuming He1,3 1Shanghai Tech University, Shanghai, China 2Lingang Laboratory, Shanghai, China 3Shanghai Engineering Research Center of Intelligent Vision and Imaging, Shanghai, China EMAIL |
| Pseudocode | Yes | Algorithm 1: Scaling Algorithm for P2OT Input: Cost matrix log P, ϵ, λ, ρ, N, K, a large value ι C [ log P, 0N], λ [λ, ..., λ, ι] K 1 K, 1 ρ] , α 1 N 1N b 1K+1, M exp( C/ϵ), f λ λ+ϵ while b not converge do a α Mb b ( β M a) f end Q diag(a)Mdiag(b) return Q[:, : K] |
| Open Source Code | Yes | Code is available at https://github.com/rhfeiyang/PPOT. |
| Open Datasets | Yes | To evaluate our method, we have established a realistic and challenging benchmark, including CIFAR100 (Krizhevsky et al., 2009), Image Net-R (abbreviated as Img Net-R) (Hendrycks et al., 2021) and i Naturalist2018 (Van Horn et al., 2018) datasets. |
| Dataset Splits | No | For Img Net-R... we split 20 images per class as the test set, leaving the remaining data as the training set (R = 13). This only specifies train/test, not validation. The phrase 'We utilize the loss on training sets for clustering head and model selection' does not describe a validation split. |
| Hardware Specification | Yes | This comparison is conducted on i Naure1000 using identical conditions (NVIDIA TITAN RTX, ϵ = 0.1, λ = 1), without employing any acceleration strategies for both. |
| Software Dependencies | No | The paper mentions using 'Vi T-B16' and the 'Adam optimizer' but does not specify version numbers for Python, PyTorch, or other relevant software libraries. |
| Experiment Setup | Yes | Specifically, we train 50 epochs and adopt the Adam optimizer with the learning rate decay from 5e-4 to 5e-6 for all datasets. The batch size is 512. Further details can be found in Appendix F. For hyperparameters, we set λ as 1, ϵ as 0.1, and initial ρ as 0.1. The stop criterion of Alg.1 is when the change of b is less than 1e-6, or the iteration reaches 1000. |