Towards an Effective Orthogonal Dictionary Convolution Strategy
Authors: Yishi Li, Kunran Xu, Rui Lai, Lin Gu1473-1481
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate it on a variety of CNNs in small-scale (CIFAR), large-scale (Image Net) and fine-grained (CUB-2002011) image classification tasks, respectively. The experimental results show that our method achieve a stable and superior improvement. |
| Researcher Affiliation | Academia | 1 School of Microelectronics, Xidian University, Xi an 710071, China 2 Chongqing Innovation Research Institute of Integrated Cirtuits, Xidian University, Chongqing 400031, China 3 RIKEN AIP, Tokyo 103-0027, Japan 4 The University of Tokyo, Tokyo, Japan |
| Pseudocode | No | The paper describes the proposed strategy and includes mathematical formulas but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (specific link, explicit statement of code release, or mention of code in supplementary materials) for the described methodology. |
| Open Datasets | Yes | We evaluate it on a variety of CNNs in small-scale (CIFAR), large-scale (Image Net) and fine-grained (CUB-2002011) image classification tasks, respectively. |
| Dataset Splits | Yes | CIFAR-10 and CIFAR-100 consists of 50k training images and 10k validation images, divided into 10 and 100 classes respectively. |
| Hardware Specification | No | The paper mentions training 'on one GPU' or 'on 8 GPUs' but does not provide specific hardware details such as GPU models (e.g., NVIDIA A100) or CPU specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9, CUDA 11.1). |
| Experiment Setup | Yes | We use the standard SGD optimizer to train our models with momentum of 0.9 and the weight decay is 4e-5. ... These models are trained with a mini-batch size of 128 on one GPU. |