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..
Candidate Pseudolabel Learning: Enhancing Vision-Language Models by Prompt Tuning with Unlabeled Data
Authors: Jiahan Zhang, Qi Wei, Feng Liu, Lei Feng
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on nine benchmark datasets with three learning paradigms demonstrate the effectiveness of our method. |
| Researcher Affiliation | Academia | 1 Singapore University of Technology and Design, Singapore 2 Nanyang Technological University, Singapore 3 University of Melbourne, Australia. |
| Pseudocode | Yes | Algorithm 1 Top-K Selection Process in Each Iteration |
| Open Source Code | Yes | Our code can be found here. |
| Open Datasets | Yes | We conduct an extensive evaluation of our method on nine classification datasets from diverse domains, including FGVC-Aircraft (Maji et al., 2013), Euro SAT (Helber et al., 2019), CUB (Wah et al., 2011), Flowers102 (Nilsback & Zisserman, 2008), RESISC45 (Cheng et al., 2017), DTD (Cimpoi et al., 2014), CALTECH-101 (Fei-Fei et al., 2004), UCF-101 (Soomro et al., 2012), and CIFAR-100 (Krizhevsky et al., 2009). |
| Dataset Splits | No | The paper provides 'Training set size' and 'Testing set size' in Table 8 for various datasets. For Semi-Supervised Learning, it mentions using 'two labeled samples per class' but does not explicitly define a separate 'validation set' with specific percentages or counts for hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, memory, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Optimizer SGD' and 'Network Vi T-B / 32' but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | Table 8: Detailed settings for experiments in Section 4. Training procedure: Network Vi T-B / 32, Batch size 64, Epoch 50 where first two epochs are set for warmup, Optimizer SGD, Momentum 0.9, Learning rate (LR) 0.02, Weight decay 5e-2, LR scheduler Cosine Annealing LR. Hyperparameters: α in intra-instance label selection (e.g., 0.60 for Flowers102), β in inter-instance label selection (e.g., 0.99 for Flowers102). |