Transfer Learning via Minimizing the Performance Gap Between Domains
Authors: Boyu Wang, Jorge Mendez, Mingbo Cai, Eric Eaton
NeurIPS 2019 | Conference 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 bwang@csd.uwo.ca Jorge A. Mendez Department of Computer and Information Science University of Pennsylvania mendezme@seas.upenn.edu Ming Bo Cai Princeton Neuroscience Insititute Princeton University mcai@princeton.edu Eric Eaton Department of Computer and Information Science University of Pennsylvania eeaton@seas.upenn.edu |
| 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. |