Supervised Representation Learning: Transfer Learning with Deep Autoencoders
Authors: Fuzhen Zhuang, Xiaohu Cheng, Ping Luo, Sinno Jialin Pan, Qing He
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct extensive experiments on three real-world image data sets to show the effectiveness of the proposed framework. Two of the three datasets are on binary classification, and the rest one is on multi-class classification. All the results of these three data sets are shown in Figure 2 and Table 3. |
| Researcher Affiliation | Academia | 1Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. {zhuangfz, heq}@ics.ict.ac.cn, luop@ict.ac.cn 2University of Chinese Academy of Sciences, Beijing, China. chengxh@ics.ict.ac.cn 3Nanyang Technological University, Singapore 639798. sinnopan@ntu.edu.sg |
| Pseudocode | Yes | Algorithm 1 Transfer Learning with Deep Autoencoders (TLDA) |
| Open Source Code | No | The paper mentions using 'authors source code3' for the baseline method m SDA (footnote 3 points to http://www.cse.wustl.edu/ mchen/), but it does not provide a link or statement about the availability of the source code for their proposed TLDA method. |
| Open Datasets | Yes | Image Net Data Set1 contains five domains, i.e., D1 (ambulance+scooter), D2 (taxi+scooter), D3 (jeep+scooter), D4 (minivan+scooter) and D5 (passenger car+scooter). Data from different domains come from different categories, e.g., taxi from D2 and jeep from D3, therefore this dataset is 1http://www.image-net.org/download-features proper for transfer learning study. Corel Data Set2 [Zhuang et al., 2010] includs two different top categories, flower and traffic. 2http://archive.ics.uci.edu/ml/datasets/Corel+Image+Features. Leaves Data Set [Mallah and Orwell, 2013] includes 100 plant species that are divided into 32 different genera, and each specie has 16 instances. |
| Dataset Splits | No | The paper describes how classification problems are constructed from datasets (e.g., 'we construct 20 (P 2 5 ) transfer learning classification problems'), but it does not provide specific percentages, absolute sample counts, or explicit cross-validation details for training, validation, or test splits for its experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using Logistic Regression, TCA, and m SDA, and refers to using the source code for m SDA (footnote 3: http://www.cse.wustl.edu/ mchen/), but it does not specify version numbers for any software, libraries, or programming languages used. |
| Experiment Setup | Yes | After some preliminary experiments, we set α = 0.5, β = 0.5, γ = 0.00001 and k = 10 for the Image Net and Corel datasets, while β = 0.05, k = 5 and γ = 0.0001 for the Leaves dataset. For m SDA, we use the authors source code3 and adopt the default parameters as reported in [Chen et al., 2012]. For TCA, the number of latent dimensions is carefully tuned, e.g., for the Corel dataset, the number is sampled from [10, 80] with interval 10, and its best results are reported. |