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..
Wrapped Cauchy Distributed Angular Softmax for Long-Tailed Visual Recognition
Authors: Boran Han
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results confirm label-aware behavior in these parameters and demonstrate WCDAS s superiority over other state-of-the-art softmaxbased methods in handling long-tailed visual recognition across multiple benchmark datasets. |
| Researcher Affiliation | Industry | Amazon Web Services, AI. Work done while at Shell.. Correspondence to: Boran Han <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Wrapped Cauchy Distributed Angular Softmax |
| Open Source Code | Yes | The code is public available. |
| Open Datasets | Yes | We also compared our approach with SOTA softmax-based methods (Section 4.4) using four large-scale long-tailed datasets: CIFAR10-LT/100-LT (Krizhevsky, 2009), Image Net-LT (Liu et al., 2019; Deng et al., 2009) and i Naturalist 2018 (Van Horn et al., 2018). |
| Dataset Splits | No | After training on the long-tailed dataset, we evaluate the models on the corresponding balanced test/validation dataset and report top-1 accuracy. |
| Hardware Specification | No | The paper does not specify the exact hardware used for experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | All models are trained using SGD optimizer with momentum 0.9, weight decay 10 4. The learning rate decays by a cosine scheduler. Unless specified, we use 90 training epochs. Other hyper-parameters are listed in Appendix Table 5. |