What is the impact of imbalanced datasets on the fine-tuning process?

Fine-tuning (deep learning) ... In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model ... Jun 26, 2024 ... PDF | Tasks like SAR ship classification suffer in deep learning due to data scarcity and class imbalance. To overcome these challenges, ... 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 ... Aug 30, 2018 ... How a TPU works. When Google designed the TPU, we built a domain-specific architecture. That means, instead of designing a general purpose ... Barakat, Basel and Huang, Qiang. 2024. 'Enhancing Transfer Learning Reliability via Block-wise Fine-tuning'. In: 2023 International Conference on Machine ... Mar 16, 2020 ... This paper presents segmentation of multiple organ regions from non-contrast CT volume based on deep learning. Also, we report usefulness of fine-tuning. Transfer learning, which aims to transfer knowledge from source tasks to a target task, is an effective solution to this problem. Fine-tuning is a popular ... 1. Robot Workflow: Machine Learning Extractor Trainer activity · 2. Document Manager: Schedule Export feature · 3. AI Center: Scheduled Auto-retraining Pipeline. We present GOODFIT, one of the first optimizers that has been specifically designed to operate on converged models that need to be incrementally fine-tuned ... AI is the new electricity and will transform and improve nearly all areas of human lives. close. Sign in with Google. Sign in with LinkedIn. Or, sign in ...