A Unified Meta-Learning Framework for Dynamic Transfer Learning
Authors: Jun Wu, Jingrui He
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various image data sets demonstrate the effectiveness of the proposed L2E framework. ... Extensive experiments on public data sets confirm the effectiveness of our proposed L2E framework. |
| Researcher Affiliation | Academia | Jun Wu , Jingrui He University of Illinois Urbana-Champaign {junwu3, jingrui}@illinois.edu |
| Pseudocode | No | The paper describes the proposed framework using equations and textual explanations, and Figure 2 illustrates the framework, but no formal pseudocode or algorithm block is provided. |
| Open Source Code | Yes | 3https://github.com/jwu4sml/L2E |
| Open Datasets | Yes | We used three publicly available image data sets: Office-31 (with 3 tasks: Amazon, Webcam and DSLR), Image-CLEF (with 4 tasks: B, C, I and P) and Caltran. |
| Dataset Splits | No | The paper mentions splitting 'training data from every historical source or target task into one training set Dtr k and one validation set Dval k' for meta-training, which indicates the use of validation data. However, it does not specify the exact split percentages, sample counts, or the methodology for creating these splits to allow for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or cloud computing resources used for the experiments. |
| Software Dependencies | No | The paper mentions 'We adopted the Res Net-18 [He et al., 2016] pretrained on Image Net as the base network,' but does not list any specific software dependencies with version numbers (e.g., deep learning frameworks like PyTorch or TensorFlow, or other libraries). |
| Experiment Setup | Yes | and set γ = 0.1 and p = 80 for all the experiments |