Transfer Learning via Minimizing the Performance Gap Between Domains
Authors: Boyu Wang, Jorge Mendez, Mingbo Cai, Eric Eaton
NeurIPS 2019 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluation on benchmark data sets shows that gap Boost significantly outperforms previous boosting-based transfer learning algorithms. |
| Researcher Affiliation | Academia | Boyu Wang Department of Computer Science University of Western Ontario EMAIL Jorge A. Mendez Department of Computer and Information Science University of Pennsylvania EMAIL Ming Bo Cai Princeton Neuroscience Insititute Princeton University EMAIL Eric Eaton Department of Computer and Information Science University of Pennsylvania EMAIL |
| Pseudocode | Yes | Algorithm 1 gap Boost |
| Open Source Code | Yes | Source code for gap Boost is available at https://github.com/bwang-ml/gap Boost. |
| Open Datasets | Yes | 20 Newsgroups This data set contains approximately 20,000 documents, grouped by seven top categories and 20 subcategories. The source and target data sets were in the same way as in [10], yielding 6 transfer learning problems. Office-Caltech This data set contains approximately 2,500 images from four distinct domains: Amazon (A), DSLR (D), Webcam (W), and Caltech (C) |
| Dataset Splits | No | The paper mentions using a 'training sample' and 'testing' data, but does not explicitly describe a validation set or how data was split for validation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions 'Logistic regression is used as the base learner' but does not specify any software dependencies with version numbers (e.g., specific Python libraries or frameworks with their versions). |
| Experiment Setup | Yes | The hyper-parameters of gap Boost were set as γmax = 1 NT as per Remark 5, ρT = 0, which corresponds to no punishment for the target data, and ρS = log 1. Logistic regression is used as the base learner for all methods, and the number of boosting iterations is set to 20. In both data sets we pre-processed the data using principal component analysis (PCA) to reduce the the feature dimension to 100. |