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..
Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training
Authors: Ming-Kun Xie, Jia-Hao Xiao, Pei Peng, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on multiple benchmark datasets with diverse configurations validate that the proposed method can achieve stateof-the-art performance. |
| Researcher Affiliation | Academia | 1Nanjing University of Aeronautics and Astronautics, Nanjing, China 2RIKEN Center for Advanced Intelligence Project, Tokyo, Japan 3The University of Tokyo, Tokyo, Japan. |
| Pseudocode | Yes | Algorithm 1 Pseudocode of PAT-I in a Py Torch-like style |
| Open Source Code | Yes | The implementation is available at https://github.com/xiemk/MLC-PAT. |
| Open Datasets | Yes | we conduct experiments on three benchmark datasets, including MS-COCO 2014 2 (Lin et al., 2014), Pascal VOC 2007 3 (Everingham et al., 2010), and Visual Genome 4 (Krishna et al., 2017). |
| Dataset Splits | Yes | MS-COCO contains 82,081 training images and 40,137 validation images for 80 classes, with an average of 2.9 labels per image. |
| Hardware Specification | Yes | We perform all experiments on Ge Force RTX 3090 GPUs. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and implementing in a 'Py Torch-like style,' but it does not specify exact version numbers for any software libraries or frameworks, such as PyTorch version. |
| Experiment Setup | Yes | We employ Adam (Kingma & Ba, 2014) optimizer and one-cycle policy scheduler to train the model with maximal learning rate of 0.0001. Furthermore, we perform exponential moving average (EMA) (Tarvainen & Valpola, 2017) for the model parameters with a decay of 0.9997. The random seed is set to 1 for all experiments. |