Deep Neural Model Fine-Tuning Task

A Deep Neural Model Fine-Tuning Task is a deep learning model transfer learning task that allows the adjustment of the parameters of a pre-trained deep learning model to improve its performance on a specific task.

• Context:
  • It can (typically) be solved by a Deep Neural Model Fine-Tuning System (that implements an model fine-tuning algorithm).
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• Example(s):
  • an Instruction-Tuning Task.
  • Data Type-Specific Deep Neural Model Fine-Tuning Task, such as:
    • Image Model Fine-Tuning Task for image recognition, where a model pre-trained on a large dataset of general images is fine-tuned for a specific task like identifying specific types of animals.
    • LLM Model Fine-Tuning Task, like of GPT-2 or BERT, for specific applications such as sentiment analysis or legal document review.
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• Counter-Example(s):
  • LoRA Technique, which involves adding trainable parameters to pre-trained models without altering the original model's weights directly.
  • A random parameter adjustments without a clear objective, which lacks the structured approach of fine-tuning towards improving task-specific performance.
  • Training a model from scratch with no pre-existing knowledge, which does not utilize transfer learning or the concept of fine-tuning.
• See: Semi-Supervised Learning, Reinforcement Learning From Human Feedback, Neural Network, Model Optimization.

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References

2023

• (Wikipedia, 2023) ⇒ https://en.wikipedia.org/wiki/Fine-tuning_(deep_learning) Retrieved:2023-10-4.