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..
A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning
Authors: Xinyi Tong, Xiangxiang Xu, Shao-Lun Huang, Lizhong Zheng
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, experiments on image classification tasks show that our approach outperforms existing transfer learning algorithms in multi-source and few-shot scenarios. 4 Experiments To validate the effectiveness of our algorithms in multi-source learning and few-shot transfer learning scenarios, we conduct a series of experiments on common datasets for image recognition, including CIFAR-10 [14], Office-31 and Office-Caltech [15]. |
| Researcher Affiliation | Academia | Xinyi Tong Tsinghua-Berkeley Shenzhen Institute Tsinghua University EMAIL Xiangxiang Xu Massachusetts Institute of Technology EMAIL Shao-Lun Huang Tsinghua-Berkeley Shenzhen Institute Tsinghua University EMAIL Lizhong Zheng Massachusetts Institute of Technology EMAIL |
| Pseudocode | Yes | Algorithm 1 Multi-Source Knowledge Transfer Algorithm 1: Input: target and source data samples {(x(i) l , y(i) l )}ni l=1 (i = 0, , k) 2: Randomly initialize α 3: repeat 4: (f , g ) arg minf,g L(α ,f,g) 5: α arg minα Ak L(α) test 6: until α converges 7: (f , g ) arg minf,g L(α ,f,g) 8: return f , g |
| 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] See supplemental material. |
| Open Datasets | Yes | We conduct multi-source transfer learning experiments on CIFAR-10 [14], which contains 50 000 training images and 10 000 testing images in 10 classes. ... Office-31 and Office-Caltech [15]. |
| Dataset Splits | No | The paper mentions training and testing data but does not explicitly specify a validation set split or methodology for it. |
| Hardware Specification | No | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [No] We use few computation resources in our work. |
| Software Dependencies | No | In our implementation of Algorithm 1, we use the CVXPY [17, 18] package for solving the non-negative quadratic programming in line 5. |
| Experiment Setup | Yes | Moreover, for each source task, 2000 images are used for training, with 1000 images per binary class, and we set target sample size n0 to n0 = 6, 20, 100, respectively. Throughout this experiment, the feature f is of dimensionality d = 10, generated by Goog Le Net [16], followed by two fully connected layers for further dimension reduction. ... In addition, the alternating iteration is stopped when the element-wise differences for α computed in two successive iterations are at most 0.05. |