Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation
Authors: Zhuohang Dang, Minnan Luo, Chengyou Jia, Guang Dai, Xiaojun Chang, Jingdong Wang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on challenging benchmarks affirm the efficacy and efficiency of SREM. |
| Researcher Affiliation | Collaboration | School of Computer Science and Technology, MOEKLINNS Laboratory, Xi an Jiaotong UniversitySGIT AI LabState Grid Corporation of ChinaUniversity of Technology SydneyBaidu IncMohamed bin Zayed University of Artificial Intelligence |
| Pseudocode | No | The paper does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of the code for the SREM methodology. |
| Open Datasets | Yes | We evaluate SREM using three image-text retrieval datasets, including COCO (Lin et al. 2014), Flickr30K (Young et al. 2014) and CC152K (Huang et al. 2021). |
| Dataset Splits | Yes | Following (Huang et al. 2021), we maintain 5K/5K and 5K/5K image-text pairs for validation/test, leaving the remainder for training. |
| Hardware Specification | Yes | We report the training overhead per epoch on CC152K using an NVIDIA Tesla A40 48G in Table 4. |
| Software Dependencies | No | The paper mentions using Adam optimizer but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | Specifically, we warm up the model for 5 epochs with Li2t c and Lt2i c to achieve initial convergence, followed by a 50 epochs training process. We employ a batch size of 128 and an Adam (Kingma and Ba 2014) optimizer with a learning rate of 2e-4 that will be decayed by 0.1 after 25 epochs. |