Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Boosted Zero-Shot Learning with Semantic Correlation Regularization
Authors: Te Pi, Xi Li, Zhongfei (Mark) Zhang
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments on two ZSL datasets show the superiority of BZ-SCR over the state-of-the-arts. |
| Researcher Affiliation | Collaboration | Te Pi1, Xi Li1,2 , Zhongfei (Mark) Zhang1 1Zhejiang University, Hangzhou, China 2Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Hangzhou, China |
| Pseudocode | Yes | Algorithm 1: Boosted Zero-shot classification with Semantic Correlation Regularization (BZ-SCR) |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code for the methodology described. It refers to external datasets but not its own code. |
| Open Datasets | Yes | We evaluate the performance of BZ-SCR on the two classic ZSL image datasets, Animal With Attributes1 (AWA) and Caltech-UCSD-Birds-2002 (CUB200). 1http://attributes.kyb.tuebingen.mpg.de/ 2http://www.vision.caltech.edu/visipedia/CUB-200-2011.html |
| Dataset Splits | Yes | For the class split, we adopt the default split (40/10 for train+val/test) for AWA and the same split as [Akata et al., 2013] (150/50 for train+val/test) for CUB200. |
| Hardware Specification | No | The paper does not explicitly mention any specific hardware used for running the experiments (e.g., GPU models, CPU types, or cloud configurations). |
| Software Dependencies | No | The paper does not provide specific version numbers for any key software components or libraries used in the implementation. |
| Experiment Setup | Yes | The shown results of BZ-SCR are achieved with ν/N = 10 4, β/N = 0.4 for AWA, and ν/N {0.025, 0.05}, β/N {0.1, 0.2} for CUB200. We implement a grid search for the tuning of the hyperparameters ν, β, and report the best performances. |