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 | Conference PDF | Archive PDF | Plain Text | 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. |