Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Hybrid Sharing for Multi-Label Image Classification
Authors: Zihao Yin, Chen Gan, Kelei He, Yang Gao, Junfeng Zhang
ICLR 2024 | Venue PDF | 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 EMAIL EMAIL |
| 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. |