A Theoretical Analysis of Metric Hypothesis Transfer Learning
Authors: Michaël Perrot, Amaury Habrard
ICML 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also present some experiments illustrating the interest of the approach in standard metric learning tasks and in a transfer learning problem where few labelled data are available. |
| Researcher Affiliation | Academia | Universit e de Lyon, Universit e Jean Monnet de Saint-Etienne, Laboratoire Hubert Curien, CNRS, UMR5516, F-42000, Saint-Etienne, France. |
| Pseudocode | No | The paper refers to "Alg. 1" and "Algorithm 5" but does not include the actual pseudocode blocks or algorithms within the provided text. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology described. |
| Open Datasets | Yes | First we start by conducting experiments on several UCI datasets (Lichman, 2013), namely breast, pima, scale and wine. |
| Dataset Splits | No | The paper mentions training on 20% of the data and testing on the remaining 80%, but does not specify a separate validation split or explicit validation methodology. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | First the positive and negative margin are respectively set to the 5th and 95th percentile of the training set possible distances computed with the source metric as proposed in (Davis et al., 2007). Next we set λ such that the two terms of Eq. 5 are equals, i.e. we balance the complexity and performance importance with respect to the source metric. |