Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Venue PDF | LLM Run Details | Input Tokens: 27,437 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,028 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |