Hybrid Sharing for Multi-Label Image Classification
Authors: Zihao Yin, Chen Gan, Kelei He, Yang Gao, Junfeng Zhang
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on two benchmark datasets, with the results demonstrating that the proposed method achieves state-of-the-art performance and yields simultaneous improvements across most labels. |
| Researcher Affiliation | Academia | Zihao Yin2,3, Chen Gan2,3, Kelei He1,3 , Yang Gao2,3 and Junfeng Zhang1,3 1 Medical School of Nanjing University 2 State Key Laboratory for Novel Software Technology, Nanjing University 3 National Institute of Healthcare Data Science, Nanjing University {zihao.yin, chengan}@smail.nju.edu.cn {hkl, gaoy, jfzhang}@nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 outlines the detailed mechanism of the Mo E mechanism that we applied. |
| Open Source Code | No | The code is available at this URL. (Abstract) / The code will be available upon acceptance. (Reproducibility Statement) |
| Open Datasets | Yes | We have performed extensive experiments on two datasets, MS-COCO and PASCAL VOC, to verify the superiority of our model. [...] MS-COCO (Lin et al., 2014) is a large dataset of 80 object classes [...] PASCAL-VOC (Everingham et al., 2015) 2007 is also a well-acknowledged dataset for multi-label image classification. |
| Dataset Splits | Yes | We have performed extensive experiments on two datasets, MS-COCO and PASCAL VOC...We follow previous work to train on train-val set and validate on test set on 2007 version. |
| Hardware Specification | Yes | All experiments are run on 4 Tesla V100-SXM2-32GB. |
| Software Dependencies | No | The paper mentions 'timm' and 'Adam optimizer' and implies Python usage but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Unless otherwise stated, the following setting is valid for all experiments. We resize all input images from any dataset to Hi Wi = 576 576... We train the model for 100 epochs using the Adam optimizer, with weight decay of 1e-2, (β1, β2) = (0.9, 0.9999), and a learning rate of 1e-4. |