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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models
Authors: Saurav Jha, Dong Gong, Lina Yao
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 42,072 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,585 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 | We evaluate our method on CIFAR100 [3, 30], Image Net100 [41, 43], Image Net-R [59], CUB200 [60], and VTAB [60]. |
| Researcher Affiliation | Collaboration | Saurav Jha1, Dong Gong1 , Lina Yao1,2 1University of New South Wales (UNSW Sydney), 2CSIRO s Data61 EMAIL; EMAIL |
| Pseudocode | Yes | Algorithm 1: A forward CLAP4CLIP pass at test step t |
| Open Source Code | Yes | Our code is available at https://github.com/srv Codes/clap4clip. |
| Open Datasets | Yes | We evaluate our method on CIFAR100 [3, 30], Image Net100 [41, 43], Image Net-R [59], CUB200 [60], and VTAB [60]. |
| Dataset Splits | Yes | we tuned our hyperparameters using a validation set comprising 10% of the CIFAR-100 training dataset. |
| Hardware Specification | Yes | All our experiments were performed on NVIDIA V100 GPUs hosted on the Gadi supercomputers of the National Computational Infrastructure (NCI Australia). |
| Software Dependencies | No | The paper mentions using SGD and refers to models like CLIP, but does not provide specific version numbers for software dependencies such as Python, PyTorch, or other libraries. |
| Experiment Setup | Yes | We train CLAP and its variants using SGD, with a batch size of 64, for 5 epochs, including 1 epoch of linear warmup. The initial learning rate (LR) is set to 1e-3 and decays with cosine annealing. At the end of each incremental task (t > 1), we perform memory consolidation training for 2 epochs, with an LR of 1e-4, on the class-balanced memory dataset. |