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..
Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual Classification
Authors: Zhaorui Tan, Xi Yang, Qiufeng Wang, Anh Nguyen, Kaizhu Huang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Theoretical analysis and experiments demonstrate that L-Reg enhances generalization across various scenarios, including multi-domain generalization and generalized category discovery. In complex real-world scenarios where images span unknown classes and unseen domains, L-Reg consistently improves generalization, highlighting its practical efficacy. |
| Researcher Affiliation | Academia | Zhaorui Tan1,2, Xi Yang1 , Qiufeng Wang1, Anh Nguyen2, Kaizhu Huang3 1 Xi an-Jiaotong Liverpool University 2 University of Liverpool 3Duke Kunshan University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/zhaorui-tan/L-Reg_Neur IPS24. |
| Open Datasets | Yes | We test L-Reg with GMDG [50] on 5 realworld benchmark datasets: PACS [32], VLCS [18], Office Home [55], Terra Incognita [7], and Domain Net [42]. |
| Dataset Splits | Yes | We operate on the Domain Bed suite [21] and leverage standard leaveone-out cross-validation as the evaluation protocol. We test L-Reg with GMDG [50] on 5 real-world benchmark datasets: PACS [32], VLCS [18], Office Home [55], Terra Incognita [7], and Domain Net [42]. Following MIRO [25] and GMDG [50], the Reg Net Y-16GF backbone with SWAG pre-training [47]) is used. |
| Hardware Specification | Yes | All experiments can be conducted on one NVIDIA Ge Force RTX 3090 GPU. |
| Software Dependencies | No | The paper does not specify version numbers for Python, PyTorch, or other libraries. It only mentions 'Reg Net Y-16GF backbone' and 'DINO (VIT-B/16)' for pre-trained models, but not software dependencies with versions. |
| Experiment Setup | Yes | We adhere to the parameters proposed by GMDG, particularly focusing on its recommended loss terms. Furthermore, we provide a detailed listing of the hyper-parameters pertaining to L-Reg, along with the tuned lr mult , as outlined in Table 9, to facilitate the reproducibility of our results. |