Hadamard Product for Low-rank Bilinear Pooling
Authors: Jin-Hwa Kim, Kyoung-Woon On, Woosang Lim, Jeonghee Kim, Jung-Woo Ha, Byoung-Tak Zhang
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct six experiments to select the proposed model, Multimodal Low-rank Bilinear Attention Networks (MLB). Each experiment controls other factors except one factor to assess the effect on accuracies. |
| Researcher Affiliation | Collaboration | Jin-Hwa Kim Interdisciplinary Program in Cognitive Science Seoul National University... Jeonghee Kim & Jung-Woo Ha NAVER LABS Corp. & NAVER Corp... Byoung-Tak Zhang School of Computer Science and Engineering & Interdisciplinary Program in Cognitive Science Seoul National University & Surromind Robotics |
| Pseudocode | No | The paper does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. Figure 1 is a schematic diagram, not pseudocode. |
| Open Source Code | Yes | The source code for the experiments is available in Github repository1. 1https://github.com/jnhwkim/Mul Low Bi VQA |
| Open Datasets | Yes | The VQA dataset (Antol et al., 2015) is used as a primary dataset, and, for data augmentation, question-answering annotations of Visual Genome (Krishna et al., 2016) are used. |
| Dataset Splits | Yes | Validation is performed on the VQA test-dev split, and model comparison is based on the results of the VQA test-standard split. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used (e.g., GPU models, CPU models, or cloud instance types) for running experiments. |
| Software Dependencies | No | The paper mentions software components like GRU, Skip-thought Vector, Bayesian Dropout, and RMSProp, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The batch size is 100, and the number of iterations is fixed to 250K. For data augmented models, a simplified early stopping is used, starting from 250K to 350K-iteration for every 25K iterations... RMSProp (Tieleman & Hinton, 2012) is used for optimization. (Also referencing Table 4 for hyperparams: 'Table 4: Hyperparameters used in MLB (single model in Table 2).') |