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How to fine-tune deep neural networks in few-shot learning?
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Deep learning has been widely used in data-intensive applications. However, training a deep neural network often requires a large data set. When there is not enough data available for training, the performance of deep learning models is even worse than that of shallow networks. It has been proved that few-shot learning can generalize to new tasks with few training samples. Fine-tuning of a deep model is simple and effective few-shot learning method. However, how to fine-tune deep learning models (fine-tune convolution layer or BN layer?) still lack deep investigation. Hence, we study how to fine-tune deep models through experimental comparison in this paper. Furthermore, the weight of the models is analyzed to verify the feasibility of the fine-tuning method.
Forward citations
Cited by 2 Pith papers
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Reassessing Layer Pruning in LLMs: New Insights and Methods
Trimming the final 25% of layers and fine-tuning the head and last three layers outperforms sophisticated pruning metrics and LoRA-based recovery for LLM compression.
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Few-Shot Learning in Video and 3D Object Detection: A Survey
A survey of few-shot learning for video and 3D object detection that reviews architectures, losses, and training strategies, but contains numerous citation errors and unsupported performance claims.
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