Order-Free RNN With Visual Attention for Multi-Label Classification
Authors: Shang-Fu Chen, Yi-Chen Chen, Chih-Kuan Yeh, Yu-Chiang Wang
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on NUS-WISE and MS-COCO datasets confirm the design of our network and its effectiveness in solving multi-label classification problems. |
| Researcher Affiliation | Academia | Shang-Fu Chen,1 Yi-Chen Chen,2 Chih-Kuan Yeh,3 Yu-Chiang Frank Wang1,2 1Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan 2Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan 3Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA |
| Pseudocode | Yes | Algorithm 1: Training of Our Proposed Model |
| Open Source Code | No | The paper does not provide any explicit statements about making its source code publicly available, nor does it include a link to a code repository. |
| Open Datasets | Yes | Our experiments on NUS-WISE and MS-COCO datasets confirm the design of our network and its effectiveness in solving multi-label classification problems. [...] NUS-WIDE is a web image dataset which includes 269,648 images [...] The training set consists of 82,783 images with up to 80 annotated object labels. |
| Dataset Splits | No | For NUS-WIDE, the paper states "150,000 images are considered for training, and the rest for testing" (a train/test split). For MS-COCO, it notes "The test set of this experiment utilizes the validation set of MS-COCO (40,504 images)" indicating the MS-COCO validation set was used as their test set. It mentions "We perform validation on the stopping threshold for beam search" but this refers to a hyperparameter tuning process, not a dedicated dataset split for validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or memory specifications). |
| Software Dependencies | No | The paper mentions using a "Res Net152 network" and an "Adam optimizer" but does not specify any software names with version numbers for libraries or frameworks used (e.g., TensorFlow, PyTorch, scikit-learn, Python version). |
| Experiment Setup | Yes | We employ the Adam optimizer with the learning rate at 0.0003, and the dropout rate at 0.8 for updating fpred. |