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..
Visual Prompt Tuning in Null Space for Continual Learning
Authors: Yue Lu, Shizhou Zhang, De Cheng, Yinghui Xing, Nannan Wang, PENG WANG, Yanning Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate the effectiveness of anti-forgetting on four classincremental benchmarks with diverse pre-trained baseline models, and our approach achieves superior performances to state-of-the-art methods. |
| Researcher Affiliation | Academia | 1 School of Computer Science, Northwestern Polytechnical University, China 2 School of Telecommunications Engineering, Xidian University, China |
| Pseudocode | Yes | An overview and algorithm of our approach are provided in Figure 6 and Algorithm 1 , respectively. |
| Open Source Code | Yes | Our code is available at https://github.com/zugexiaodui/VPTin NSfor CL . |
| Open Datasets | Yes | Our experiments are conducted across 4 classincremental benchmarks: 10and 20-split CIFAR-100, 10-split Image Net-R [39] and 10-split Domain Net [38]. |
| Dataset Splits | Yes | 25% samples of the training data in each dataset are picked as a validation set for searching optimal hyper-parameters. |
| Hardware Specification | Yes | The experiments are performed on a server with 128 GB RAM and four NVIDIA RTX 4090 GPUs. |
| Software Dependencies | No | We implement our approach in Py Torch [24] with the timm library [41]. Specific version numbers for PyTorch and timm are not provided. |
| Experiment Setup | Yes | For the VPT-based models, we use the Adam optimizer [16] with β1 = 0.9, β2 = 0.999 and a weight decay of 5 10 5 to train 100 epochs with an initial learning rate of 0.01 and a batch size of 256 on all benchmarks. The learning rate is scaled by a factor of 0.1 at the 50-th and 80-th epoch. |