LPT: Long-tailed Prompt Tuning for Image Classification
Authors: Bowen Dong, Pan Zhou, Shuicheng YAN, Wangmeng Zuo
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that on various long-tailed benchmarks, with only 1.1% extra trainable parameters, LPT achieves comparable or higher performance than previous whole model fine-tuning methods, and is more robust to domain-shift. |
| Researcher Affiliation | Academia | Bowen Dong1 Pan Zhou2 Shuicheng Yan2 Wangmeng Zuo1,3 1Harbin Institute of Technology 2National University of Singapore 3 Peng Cheng Laboratory {cndongsky,panzhou3,shuicheng.yan}@gmail.com, wmzuo@hit.edu.cn |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is publicly available at https://github.com/DongSky/LPT. |
| Open Datasets | Yes | CIFAR100-LT is a subset of the original CIFAR-100 (Krizhevsky, 2009)... Places-LT (Liu et al., 2019) is the subset of Places-365 dataset Zhou et al. (2017a)... i Naturalist 2018 (Van Horn et al., 2018)... Image Net-Sketch (Wang et al., 2022)... Image Net-LT (Liu et al., 2019). |
| Dataset Splits | No | The paper mentions using train, val, and test sets for Places-LT and iNaturalist 2018, and generating long-tailed training data for CIFAR100-LT, but it does not specify the exact split percentages, sample counts, or cite a predefined split methodology for these datasets, which are necessary for full reproducibility of data partitioning. |
| Hardware Specification | Yes | All programs are implemented by Py Torch toolkit (Paszke et al., 2019), and all experiments are conducted on a single RTX A6000 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch toolkit (Paszke et al., 2019)' but does not provide specific version numbers for PyTorch or other software dependencies. |
| Experiment Setup | Yes | For shared prompt, we simply set the default length of prompt as 10... we use SGD optimizer with momentum of 0.9... the initial learning rate is set to be 0.002 B 256... we set the weight decay as 1e-4... For Places-LT... we optimize phase 1 and phase 2 of LPT for E = 40 epochs... For asymmetric GCL loss, we set λ+ and λ as 0 and 4, respectively. And for phase 2, we set the initialized weight γ used in {I}ins as 0.5. In all experiments, the training and testing images are resized to 224 224. |