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Pre-Training and Fine-Tuning
In this chapter, we focus on modern parameter-based methods: the pre-training and fine-tuning approach. We will also step into deep transfer learning...
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SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels
Pre-trained vision transformers have strong representations benefit to various downstream tasks. Recently many parameter-efficient fine-tuning (PEFT)...
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Improving BERT Fine-Tuning via Self-Ensemble and Self-Distillation
Fine-tuning pre-trained language models like BERT have become an effective way in natural language processing (NLP) and yield state-of-the-art...
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Multi-phase Fine-Tuning: A New Fine-Tuning Approach for Sign Language Recognition
In this paper, we propose multi-phase fine-tuning for tuning deep networks from typical object recognition to sign language recognition (SLR). It...
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How Does Fine-Tuning Impact Out-of-Distribution Detection for Vision-Language Models?
Recent large vision-language models such as CLIP have shown remarkable out-of-distribution (OOD) detection and generalization performance. However,...
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Provenance-Based Dynamic Fine-Tuning of Cross-Silo Federated Learning
Federated Learning (FL) is a distributed technique that allows multiple users to train models collaboratively without accessing private and sensitive...
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Speeding-up and compression convolutional neural networks by low-rank decomposition without fine-tuning
With the rapid development of convolutional neural network (CNN), the accuracy of CNN has been significantly improved, which also brings great...
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Getting it right: the limits of fine-tuning large language models
The surge in interest in natural language processing in artificial intelligence has led to an explosion of new language models capable of engaging in...
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Genetic-efficient fine-tuning with layer pruning on multimodal Covid-19 medical imaging
Medical image analysis using multiple modalities refers to the process of analyzing and extracting information from more than one type of image in...
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Detection of abnormal fish by image recognition using fine-tuning
Fishermen need to remove abnormal or dead fish for the prevention of viral infection. However, the identification of diseased fish is more ambiguous...
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Fine-tuning pre-trained neural networks for medical image classification in small clinical datasets
Convolutional neural networks have been effective in several applications, arising as a promising supporting tool in a relevant Dermatology problem:...
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Enhancing multiple-choice question answering through sequential fine-tuning and Curriculum Learning strategies
With the transformer-based pre-trained language models, multiple-choice question answering (MCQA) systems can reach a particular level of...
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A transformer fine-tuning strategy for text dialect identification
Online medical consultation can significantly improve the efficiency of primary health care. Recently, many online medical question–answer services...
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Mitigating Fine-Grained Hallucination by Fine-Tuning Large Vision-Language Models with Caption Rewrites
Large language models (LLMs) have shown remarkable performance in natural language processing (NLP) tasks. To comprehend and execute diverse human...
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An efficient pruning and fine-tuning method for deep spiking neural network
Spiking Neural Networks (SNNs) demonstrate low hardware and power consumption due to their inherent sparse spike-based computing characteristics,...
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Parameter-efficient fine-tuning of pre-trained code models for just-in-time defect prediction
Software engineering workflows use version control systems to track changes and handle merge cases from multiple contributors. This has introduced...
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Progressive loss-aware fine-tuning stepwise learning with GAN augmentation for rice plant disease detection
Modern technology like Artificial Intelligence (AI) must be used in the agricultural sector if sustainable agricultural output is to be achieved. One...
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Fine-Tuning Large Enterprise Language Models via Ontological Reasoning
Large Language Models (LLMs) exploit fine-tuning as a technique to adapt to diverse goals, thanks to task-specific training data. Task specificity...
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CUTE: A Collaborative Fusion Representation-Based Fine-Tuning and Retrieval Framework for Code Search
Code search aims at searching semantically related code snippets from the large-scale database based on a given natural descriptive query....