Adaptive Vision-Language Prompt Learners for Learning with Noisy Labels

Training deep learning models requires manual labelling of a large volume of diverse data that is a tedious and time-consuming process. As humans are prone to errors, large-scale data labelling often introduces label noise, leading to degradation in the performance of deep neural networks. Recently, pre-trained models on extensive multi-modal data have shown remarkable performance in computer vision tasks. However, their use to tackle the problem of learning with noisy labels is still in its infancy, due to high computational complexity and training costs. In this work, we propose a novel approach, AVL-Prompter, to effectively leverage vision-language-pre-trained models for learning with noisy labels. The key idea of our method is the use of shared deep learnable prompts between visual and textual encoders, allowing us to effectively adapt large V-L models to the downstream task of learning with noisy labels. Our technique exhibits superior performance, particularly in high-noise settings, outperforming state-of-the-art methods in several datasets with synthetic and real label noise. Our contribution comes from a novel, simple, but highly efficient methodological path to learning with noisy labels while remaining straightforward to implement. The code is available at https://github.com/bhalajin/AVL-Prompter.