Revealing Distribution Discrepancy by Sampling Transfer in Unlabeled Data
Authors: Zhilin Zhao, Longbing Cao, Xuhui Fan, Wei-Shi Zheng
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental results This section presents a comparative analysis of I-Div 2 against existing methods for evaluating the distribution discrepancy between training and test samples. The detailed experimental setups are presented in Appendix D.1. |
| Researcher Affiliation | Academia | Zhilin Zhao1,2 Longbing Cao1 Xuhui Fan1 Wei-Shi Zheng2,3 1 School of Computing, Macquarie University, Australia 2 School of Computer Science and Engineering, Sun Yat-sen University, China 3 Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China |
| Pseudocode | Yes | Algorithm 1 Importance divergence |
| Open Source Code | Yes | The source code is publicly available at: https://github.com/Lawliet-zzl/I-div. |
| Open Datasets | Yes | We utilize two datasets, CIFAR10 [31] and SVHN [32], each comprising ten semantically unique classes. |
| Dataset Splits | No | The paper discusses training and test datasets but does not explicitly specify the use of a validation set or details about its split. |
| Hardware Specification | Yes | All experiments are conducted using the Py Torch framework on a single 64GB GPU. |
| Software Dependencies | No | The paper mentions using the "Py Torch framework" and other tools like "CLIP", "Res Net50", and "Vi T-B/16", but it does not provide specific version numbers for any of these software dependencies. |
| Experiment Setup | Yes | Unless otherwise specified, we set λ = 1 and γ = 1. ... Unless otherwise noted, our experiments use a standard sample size of M = 1000. We generate 100, 000 tuples to achieve significant distribution discrepancy in the positive pairs and minimal discrepancy in the negative pairs. |