Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Knowledge Transfer with Interactive Learning of Semantic Relationships
Authors: Jonghyun Choi, Sung Ju Hwang, Leonid Sigal, Larry Davis
AAAI 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,500 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,576 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the proposed model in a few-shot multi-class classification scenario, where we measure classification performance on a set of target classes, with few training instances, by leveraging and transferring knowledge from anchor classes, that contain larger set of labeled instances. |
| Researcher Affiliation | Collaboration | Jonghyun Choi University of Maryland College Park, MD EMAIL Sung Ju Hwang UNIST Ulsan, Korea EMAIL Leonid Sigal Disney Research Pittsburgh, PA EMAIL Larry S. Davis University of Maryland College Park, MD EMAIL |
| Pseudocode | Yes | We summarize the overall procedure in Algorithm 1 and describe the steps in the following subsections. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology. |
| Open Datasets | Yes | We use two object categorization datasets: 1) Animals with Attributes (AWA) (Lampert, Nickisch, and Harmeling 2009), which consists of 50 animal classes and 30,475 images, 2) Image Net-50 (Hwang, Grauman, and Sha 2013), which consists of 70,380 images of 50 categories. |
| Dataset Splits | Yes | For testing and validation set, we use a 50/50 split of the remaining samples, excluding the training samples. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | We evaluate the performance of knowledge transfer by measuring the classification accuracy of each model on the target classes in a challenging set-up that has only a few training samples (2, 5 and 10 samples per class, few-shot learning) with a prior learned with anchor classes that have a larger numbers of training samples (30 samples per class). |