Vector Quantization Prompting for Continual Learning
Authors: Li Jiao, Qiuxia LAI, YU LI, Qiang Xu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that VQPrompt outperforms state-of-the-art continual learning methods across a variety of benchmarks under the challenging class-incremental setting. |
| Researcher Affiliation | Academia | Li Jiao1, Qiuxia Lai1 , Yu Li2, Qiang Xu3 1 Communication University of China 2 Harbin Institute of Technology, Shenzhen 3 The Chinese University of Hong Kong |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/jiaolifengmi/VQ-Prompt. |
| Open Datasets | Yes | Datasets. We consider three representative benchmarks for evaluating CIL. Image Net-R [14] includes 200-class images... Split CIFAR-100 randomly splits the original CIFAR-100 [25] into 10 disjoint tasks... Split CUB-200 is built on CUB-200-2011 [57], a fine-grained classification dataset... |
| Dataset Splits | Yes | Following Dual Prompt [61] and CODA-Prompt [50], we use 20% of the training data as validation data, and perform hyperparameters tuning on it. |
| Hardware Specification | Yes | Each experiment is run on a single NVIDIA Ge Force RTX 4090 GPU. |
| Software Dependencies | No | The paper mentions using Adam W optimizer with specific beta values but does not provide version numbers for key software components like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | Our method is trained using an Adam W optimizer [35] with an initial learning rate of 0.0025 and a cosine decay schedule. The batch size is 128 for Split CIFAR-100 and Split CUB-200, and 64 for Image Net-R. The number of epochs is set to be 20 for training on all three datasets. The classifier bias mitigation process described in 4.3 requires ten epochs of training. |