Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual Classification
Authors: Zhaorui Tan, Xi Yang, Qiufeng Wang, Anh Nguyen, Kaizhu Huang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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. |