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..
Semi-transductive Learning for Generalized Zero-Shot Sketch-Based Image Retrieval
Authors: Ce Ge, Jingyu Wang, Qi Qi, Haifeng Sun, Tong Xu, Jianxin Liao
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on two large-scale benchmarks and four evaluation metrics. The results show that our method is superior over the state-of-the-art competitors in the challenging GZS-SBIR task. |
| Researcher Affiliation | Collaboration | State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing 100876, China EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We employ two widely used SBIR datasets: Sketchy (Sangkloy et al. 2016) and TU-Berlin (Eitz, Hays, and Alexa 2012). |
| Dataset Splits | No | The paper mentions splitting datasets into seen and unseen classes and describes the generalized test set, but it does not specify the training/validation splits or percentages for the training phase, nor explicit details about a validation set beyond implying its use for early stopping. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments, such as CPU/GPU models or memory specifications. |
| Software Dependencies | Yes | The whole model is implemented on top of Py Torch (Paszke et al. 2019) |
| Experiment Setup | Yes | The feature dimension of the embedding space is set to 1024D. The weighting factors for each dataset are determined by grid search with ω1 [0.01, 1] and ω2 [0.001, 10]. For Sketchy, ω1 = 0.5, ω2 = 0.1, and for TU-Berlin ω1 = 0.5, ω2 = 0.5. The margin hyperparameters in Lrank (Eq. 3) and Ltrans (Eq. 11) are empirically set to = 0.1 and δ = 0.01, respectively. The whole model is implemented on top of Py Torch (Paszke et al. 2019) and is trained end-to-end by stochastic gradient descent with learning rate 1e-3 and a mini-batch size 20. The early stopping strategy is adopted to combat overfitting. |