How can we develop metrics and benchmarks to evaluate the societal impact of fine-tuned models?

LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks ... Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less ... Learn the fundamentals of finetuning a large language model (LLM). Understand how finetuning differs from prompt engineering, and when to use both. Apr 18, 2022 ... In this article, we investigate the transfer learning of deep convolutional neural networks for the detection of maize leaf diseases. Modifying the "initial_epoch" arg when doing fine tuning and transfer learning. ... mrdbourke / tensorflow-deep-learning Public. Notifications You must be signed ... Nov 7, 2023 ... When fine-tuning, it's common to adjust the deeper layers of the model while keeping the initial layers fixed. The rationale is that the initial ... A fine-tuning deep learning framework to palliate data distribution shift effects in rotary machine fault detection. Nima Rezazadeh, Perfetto Donato, Mario ... Fine-Tuning in Modern Machine Learning: Principles and Scalability. Fanghui Liu, Grigorios Chrysos, Beidi Chen, Rebekka Burkholz, Saleh Soltan, Angeliki ... Nov 17, 2022 ... 2 Answers 2 ... No, you don't need to retrain the entire model. Fine-tuning refers to taking the weights trained in the general model and then ... Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning. IEEE Trans Med Imaging. 2018 Jul;37(7):1562-1573. doi: 10.1109 ... We investigate generalization properties of fine-tuning to understand the problem of overfitting, which often happens in practice.