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..
Comprehensive Semi-Supervised Multi-Modal Learning
Authors: Yang Yang, Ke-Tao Wang, De-Chuan Zhan, Hui Xiong, Yuan Jiang
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical studies show the superior performances of CMML on real-world data in terms of various criteria. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University 2Rutgers University |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. The methods are described mathematically and in prose. |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing the code or a link to a code repository. |
| Open Datasets | Yes | we ο¬rst experiment on 4 public real-world datasets, i.e., FLICKR25K [Huiskes and Lew, 2008], IAPR TC-12 [Escalante et al., 2010], MS-COCO [Lin et al., 2014] and NUS-WIDE [Chua et al., 2009]. Besides, we also experiment on 1 real-world complex article dataset, i.e., WKG Game-Hub [Yang et al., 2018a] |
| Dataset Splits | Yes | For each dataset, we randomly select 33% of the data for test set and the remaining instances are used for training. And for training data, we randomly choose 30% as the labeled data, and the left 70% as unlabeled ones. |
| Hardware Specification | Yes | We run the following experiments with the implementation of an environment on NVIDIA K80 GPUs server, and our model can be trained about 290 images per second with a single K80 GPGPU. |
| Software Dependencies | No | The paper mentions using Resnet18 and fully connected networks, but does not provide specific version numbers for any software dependencies like deep learning frameworks (e.g., TensorFlow, PyTorch) or programming languages. |
| Experiment Setup | Yes | Image encoder is implemented with Resnet18 [He et al., 2015], the text utilizes fully connected network. The parameter Ξ» in the training phase is tuned in {0.1, 0.2, ..., 0.9}. When the variation between the objective values of Eq. 6 is less than 10^-4 in iterations, we consider CMML converges. |