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..
LPT: Long-tailed Prompt Tuning for Image Classification
Authors: Bowen Dong, Pan Zhou, Shuicheng YAN, Wangmeng Zuo
ICLR 2023 | Venue PDF | 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 EMAIL, EMAIL |
| 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. |