Comprehensive Semi-Supervised Multi-Modal Learning
Authors: Yang Yang, Ke-Tao Wang, De-Chuan Zhan, Hui Xiong, Yuan Jiang
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | 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 first 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 λ 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. |