DIANet: Dense-and-Implicit Attention Network
Authors: Zhongzhan Huang, Senwei Liang, Mingfu Liang, Haizhao Yang4206-4214
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on benchmark datasets show that the DIA-LSTM unit is capable of emphasizing layer-wise feature interrelation and leads to significant improvement of image classification accuracy. |
| Researcher Affiliation | Academia | 1Tsinghua University, 2Purdue University, 3Northwestern University, 4National University of Singapore |
| Pseudocode | Yes | Algorithm 1 Calculate feature integration using Gini importance by Random Forest |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | Yes | We conduct experiments on CIFAR10, CIFAR100 (Krizhevsky and Hinton 2009) and Image Net 2012 (Russakovsky et al. 2015) |
| Dataset Splits | Yes | CIFAR10 or CIFAR100 has 50k train images and 10k test images of size 32 by 32... Image Net 2012 (Russakovsky et al. 2015) comprises 1.28 million training and 50k validation images from 1000 classes, and the random cropping of size 224 by 224 is used in our experiments. |
| Hardware Specification | Yes | The experiments are conducted on a single GPU with batch size 128 and initial learning rate 0.1. ... and the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | The experiments are conducted on a single GPU with batch size 128 and initial learning rate 0.1. ... The reduction ratio is the only hyper-parameter in DIANet. ... We choose different activation functions in the output layer of LSTM in Figure 4 (Bottom) and different numbers of stacking LSTM cells to explore the effects of these two factors. |