Advancing Cross-domain Discriminability in Continual Learning of Vision-Language Models
Authors: Yicheng Xu, Yuxin Chen, Jiahao Nie, Yusong Wang, HUIPING ZHUANG, Manabu Okumura
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results affirm RAIL s state-of-the-art performance in both X-TAIL and existing Multi-domain Task-Incremental Learning settings. |
| Researcher Affiliation | Academia | Yicheng Xu1 Institute of Science Tokyo 2University of California Berkeley 3Nanyang Technological University 4South China University of Technology 5Greater Bay Area Institute for Innovation, Hunan University, China |
| Pseudocode | Yes | The pseudo-codes of both training and testing algorithms are provided in Appendix A. |
| Open Source Code | Yes | The code is released at https://github.com/linghan1997/ Regression-based-Analytic-Incremental-Learning. |
| Open Datasets | Yes | we select 10 different image-classification datasets from different domains for our setting: Aircraft [24], Caltech101 [25], DTD [26], Euro SAT [27], Flowers [28], Food [29], MNIST [30], Oxford Pet [31], Stanford Cars [32], and SUN397 [33]. |
| Dataset Splits | Yes | The optimal values are determined by minimizing the regression error on the validation set of the first domain, without access to future domains. |
| Hardware Specification | Yes | All the results are conducted on Ubuntu 20.04 with Intel Core i9-13900K CPU with a single RTX 4090Ti GPU by the average of 3 runs. |
| Software Dependencies | No | The paper mentions using a pre-trained CLIP model and an Ubuntu operating system, but does not provide specific version numbers for software dependencies or libraries (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We conduct a grid search for the regularization parameter λ over the range 10 6, 10 5, ..., 1 and the RBF kernel bandwidth over the range 10 6, 10 5, ..., 10. |