OpenAUC: Towards AUC-Oriented Open-Set Recognition
Authors: Zitai Wang, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, an end-to-end learning method is proposed to minimize the Open AUC risk, and the experimental results on popular benchmark datasets speak to its effectiveness. |
| Researcher Affiliation | Collaboration | Zitai Wang1,2 Qianqian Xu3 Zhiyong Yang4 Yuan He5 Xiaochun Cao6,1 Qingming Huang4,3,7,8 1 SKLOIS, Institute of Information Engineering, CAS 2 School of Cyber Security, University of Chinese Academy of Sciences 3 Key Lab. of Intelligent Information Processing, Institute of Computing Tech., CAS 4 School of Computer Science and Tech., University of Chinese Academy of Sciences 5 Alibaba Group 6 School of Cyber Science and Tech., Shenzhen Campus, Sun Yat-sen University 7 BDKM, University of Chinese Academy of Sciences 8 Peng Cheng Laboratory |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. Methodological steps are described in prose and mathematical formulations. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | Following the protocol in [13] and [14], the experiments are conducted on the following datasets: (1) MNIST1 [32], SVHN2 [33] and CIFAR103 [34]... (2) CIFAR+10 and CIFAR+50... (3) Tiny Image Net4 [35]... (4) Fine-grained datasets such as CUB5 [36]... 1http://yann.lecun.com/exdb/mnist/. Licensed GPL3. 2http://ufldl.stanford.edu/housenumbers/. Licensed GPL3. 3https://www.cs.toronto.edu/~kriz/cifar.html. Licensed MIT. 4http://cs231n.stanford.edu/tiny-imagenet-200.zip. Licensed MIT. 5https://www.vision.caltech.edu/datasets/cub_200_2011/. Licensed MIT. |
| Dataset Splits | Yes | The experiments are conducted on five different splits of each dataset, and we report the standard divation in Tab.2. |
| Hardware Specification | No | The main paper does not explicitly state specific GPU or CPU models, memory details, or detailed computer specifications. While the 'Ethics Statement' claims these details are provided, they are not present in the provided paper content. |
| Software Dependencies | No | The paper mentions software like PyTorch, NumPy, and Scikit-learn in its references, but it does not specify the exact version numbers of these or other ancillary software components used for the experiments in the provided text. |
| Experiment Setup | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] |