What are the different continual learning strategies that involve fine tuning?

Jun 3, 2019 ... Fine-tuning is a super-powerful method to obtain image classifiers on your own custom datasets from pre-trained CNNs (and is even more powerful ... Jun 1, 2022 ... Award Share. Community Info Section. r/deeplearning. Join. Deep Learning. 192K Members. 56 Online. Top 1% Rank by size. More posts you may like. Feb 28, 2025 ... For the learning rate during fine-tuning, we often use a value up to 10 times smaller than usual. As a result, our model will try to adapt ... Dec 17, 2024 ... Qualitative review clearly showed its insufficiency; prone to detect patients' faces as symmetric, and unable to detect closure of the eyes. Dec 16, 2024 ... ... exploration of strongly correlated systems using NQS, mirroring the success of fine-tuning in machine learning and natural language processing. Mar 20, 2023 ... With fine-tuning, you freeze the weights for all the pretrained layers except for one. Then, through edge training, the weights for the single ... Deep learning practice is increasingly driven by powerful foundation models (FM), pre-trained at scale and then fine-tuned for specific tasks of interest. Jul 17, 2023 ... As a machine learning practitioner, you might often find yourself in a situation where you're fine-tuning a pre-trained model for your specific ... Jan 25, 2024 ... Learn Machine Learning. Welcome to r/learnmachinelearning - a community of learners and educators passionate about machine learning! This is ... We investigate generalization properties of fine-tuning to understand the problem of overfitting, which often happens in practice.