DropCov: A Simple yet Effective Method for Improving Deep Architectures
Authors: Qilong Wang, Mingze Gao, Zhaolin Zhang, Jiangtao Xie, Peihua Li, Qinghua Hu
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on various benchmarks (i.e., Image Net-1K, Image Net-C, Image Net-A, Stylized-Image Net, and i Nat2017) show our Drop Cov is superior to the counterparts in terms of efficiency and effectiveness, and provides a simple yet effective method to improve performance of deep architectures involving both deep convolutional neural networks (CNNs) and vision transformers (Vi Ts). |
| Researcher Affiliation | Academia | Qilong Wang1,3, Mingze Gao1, Zhaolin Zhang1, Jiangtao Xie2, Peihua Li2, Qinghua Hu1,3, 1Tianjin University, China, 2Dalian University of Technology, China, 3 Haihe Laboratory of Information Technology Application Innovation, Tianjin, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | Source code (e.g., Py Torch [33], Paddle Paddle and Mindspore) will be available at https://github.com/mingze G/Drop Cov. |
| Open Datasets | Yes | Extensive experiments on various benchmarks (i.e., Image Net-1K, Image Net-C, Image Net-A, Stylized-Image Net, and i Nat2017) show our Drop Cov is superior to the counterparts in terms of efficiency and effectiveness, and provides a simple yet effective method to improve performance of deep architectures involving both deep convolutional neural networks (CNNs) and vision transformers (Vi Ts). |
| Dataset Splits | Yes | All models are optimized using the same hyper-parameter settings and data augmentations as suggested in [17, 29], where stochastic gradient descent (SGD) with initial learning rate (lr) of 0.1 is used to train the networks within 100 epochs and lr is decayed by 10 every 30 epochs. We conduct experiments on Image Net-1K (IN-1K) using backbone of Res Net-18. |
| Hardware Specification | Yes | All programs run a sever equipped with eight Nvidia RTX 3090 GPUs and 128G RAM. |
| Software Dependencies | No | Source code (e.g., Py Torch [33], Paddle Paddle and Mindspore) will be available at https://github.com/mingze G/Drop Cov. The paper lists software names but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | All models are optimized using the same hyper-parameter settings and data augmentations as suggested in [17, 29], where stochastic gradient descent (SGD) with initial learning rate (lr) of 0.1 is used to train the networks within 100 epochs and lr is decayed by 10 every 30 epochs. |