Transfer Learning via Learning to Transfer
Authors: Ying WEI, Yu Zhang, Junzhou Huang, Qiang Yang
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also theoretically analyse the algorithmic stability and generalization bound of L2T, and empirically demonstrate its superiority over several state-of-the-art transfer learning algorithms.Datasets We evaluate the L2T framework on two image datasets, Caltech-256 (Griffin et al., 2007) and Sketches (Eitz et al., 2012).We compare L2T with the following nine baseline algorithms in three classes:Non-transfer: Original builds a model using labeled data in a target domain only.Common latent space based transfer learning algorithms: TCA (Pan et al., 2011), ITL (Shi & Sha, 2012), CMF (Long et al., 2014), LSDT (Zhang et al., 2016), STL (Raina et al., 2007), DIP (Baktashmotlagh et al., 2013) and SIE (Baktashmotlagh et al., 2014).Manifold ensemble based algorithms: GFK (Gong et al., 2012).The eight feature-based transfer learning algorithms also constitute the base set A. Based on feature representations obtained by different algorithms, we use the nearestneighbor classifier to perform three-class classification for the target domain. |
| Researcher Affiliation | Collaboration | 1Hong Kong University of Science and Technology, Hong Kong 2Tencent AI Lab, Shenzhen, China. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. It describes the framework and optimization problems mathematically. |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the described methodology. |
| Open Datasets | Yes | Datasets We evaluate the L2T framework on two image datasets, Caltech-256 (Griffin et al., 2007) and Sketches (Eitz et al., 2012). |
| Dataset Splits | Yes | In total, we generate 1,000 training pairs for preparing transfer learning experiences, 500 validation pairs to determine hyperparameters of the reflection function, and 500 testing pairs to evaluate the reflection function. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | In building the reflection function, we use 33 RBF kernels with the bandwidth δk in the range of [2 8η : 20.5η : 28η] where η = 1 nsente Ne Ne e=1 ns e,nt e i,j=1 xs ei W xt ej W 2 2 follows the median trick (Gretton et al., 2012a). |