Adaptive Convolutional ReLUs
Authors: Hongyang Gao, Lei Cai, Shuiwang Ji3914-3921
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our proposed Conv Re LU has consistently better performance compared to Re LU, Leaky Re LU, and PRe LU. In addition, the partial replacement strategy is shown to be effective not only for our Conv Re LU but also for Leaky Re LU and PRe LU. |
| Researcher Affiliation | Academia | Hongyang Gao,1 Lei Cai,2 Shuiwang Ji1 1Texas A&M University, College Station, TX, USA 2Washington State University, Pullman, WA, USA {hongyang.gao, sji}@tamu.edu, lei.cai@wsu.edu |
| Pseudocode | No | The paper describes methods in text and mathematical formulas but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | Image classification datasets: For image classification tasks, we use three image datasets including Cifar10, Cifar100 (Krizhevsky, Hinton, and others 2009), and Tiny Image Net (Yao and Miller 2015). Text classification datasets: For text classification tasks, we choose three datasets; those are, MR, AG s News, and Yelp Full. MR is a Movie Review dataset (Pang and Lee 2005), which includes positive and negative reviews for sentiment classification. AG s News is a topic classification dataset with four topics: World, Sports, Business, and Sci/Tech (Zhang, Zhao, and Le Cun 2015). Yelp Full is formed based on the Yelp Dataset Challenge 2015 (Zhang, Zhao, and Le Cun 2015). |
| Dataset Splits | Yes | Cifar10 and Cifar100 contain natural images with 32 32 pixels. Cifar10 consists of images from 10 classes, while the images in Cifar100 are drawn from 100 classes. Both datasets contain 50,000 training and 10,000 testing images. Tiny Image Net dataset is a tiny version of Image Net dataset (Deng et al. 2009). It has 200 classes, each of which contains 500 training, 50 validation, and 50 testing images. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions the use of the SGD optimizer and deep learning frameworks like Dense Net and VGG-like networks, but it does not specify software dependencies with version numbers (e.g., specific library versions or framework versions). |
| Experiment Setup | Yes | In training, the SGD optimizer (Le Cun, Bengio, and Hinton 2015) is used with a learning rate that starts from 0.1 and decays by 0.1 at the 150th and 250th epoch. The batch size is 128. These hyper-parameters are tuned on the Cifar10 and AG s News datasets, then applied on other datasets. |