What are the challenges of fine tuning models for tasks with subjective or ambiguous labels?

Feb 13, 2025 ... Fine-tuning is a form of transfer learning where the pre-trained model is adjusted to better fit the new data. There are several steps involved ... Jun 26, 2024 ... PDF | Tasks like SAR ship classification suffer in deep learning due to data scarcity and class imbalance. To overcome these challenges, ... We investigate generalization properties of fine-tuning to understand the problem of overfitting, which often happens in practice. Dec 10, 2024 ... Fine-tuning improves the performance of a pre-trained model for a specific use case by tailoring it to a specific task. The process adjusts the ... This paper examines how well deep networks can diagnose various cancers using histopathology image processing. Jun 25, 2024 ... The key is to adjust the network to work well with the specific data you're using, so it can make good predictions. Sep 21, 2023 ... What is Fine-Tuning? Fine-tuning in deep learning is a form of transfer learning. It involves taking a pre-trained model, which has been ... Sep 30, 2020 ... Experiment Manager is an app for training, fine-tuning, and explaining your deep learning networks under a variety of initial conditions. This tutorial will walk through the process of transfer learning from a pre-trained network and a minimal approach to hyperparamater tuning. This FITML workshop aims to contribute to the recent radical paradigm shift for fine-tuning in modern machine learning, theoretically, computationally, and ...