machengcheng2016/Subspace-Prompt-Learning
Official code for "Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models" (TCSVT'2023)
This project helps machine learning researchers and practitioners improve the performance of vision-language models like CLIP when adapting them to new image classification tasks with limited data. It takes an existing "prompt-tuned" model and fine-tunes it further to prevent overfitting and enhance generalization. The output is a more robust and accurate image classification model, particularly useful for those working with few-shot learning or novel object categories.
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Use this if you are a machine learning researcher or engineer working with vision-language models and want to prevent overfitting and improve generalization when fine-tuning them for new image recognition tasks, especially with limited data.
Not ideal if you are looking for a plug-and-play solution for general image classification without prior experience in prompt tuning or vision-language model adaptation.
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Dec 27, 2023
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