Effective Abstract Reasoning with Dual-Contrast Network
Authors: Tao Zhuo, Mohan Kankanhalli
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the RAVEN and PGM datasets show that DCNet outperforms the state-of-the-art methods by a large margin of 5.77%. Further experiments on few training samples and model generalization also show the effectiveness of DCNet. |
| Researcher Affiliation | Academia | Tao Zhuo, Mohan Kankanhalli School of Computing, National University of Singapore zhuotao@nus.edu.sg, mohan@comp.nus.edu.sg |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/visiontao/dcnet. |
| Open Datasets | Yes | Similar to the previous works (Zhang et al., 2019b; Zheng et al., 2019; Wang et al., 2020), we conduct experiments on the RAVEN (Zhang et al., 2019a) and PGM (Santoro et al., 2018). |
| Dataset Splits | Yes | In each configuration, the dataset is randomly split into three parts, 6 folds for training, 2 for validation, and the remaining 2 for testing. |
| Hardware Specification | Yes | In addition, all models are trained and evaluated on a single GPU of NVIDIA Ge Force 1080 Ti with 11 GB memory. |
| Software Dependencies | No | The paper mentions the use of "Adam optimizer (Kingma & Ba, 2015)" but does not specify software dependencies with version numbers (e.g., Python, PyTorch/TensorFlow versions). |
| Experiment Setup | Yes | During the training phase, a mini-batch size of 32 with Adam optimizer (Kingma & Ba, 2015) is employed to learn the network parameters, and the learning rate is set to 0.001 and fixed. |