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 [1].
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. |