例如,针对Fine-tuning,可以进一步研究模型的可解释性、模型间的迁移性等问题;而在Embedding方面,可以考虑多模态数据的嵌入、更加复杂的语义关系建模等研究方向。 总的来说,Fine-tuning和Embedding是深度学习中两个重要且相互关联的概念。Fine-tuning通过在预训练模型的基础上进行微调,使模型适应特定任务的数据和要求;而Em...
In this paper, we explore an alternative to fine-tuning: rewinding. Rather than continuing to train the resultant pruned network (fine-tuning), rewind the remaining weights to their values from earlier in training, and re-train the resultant network for the remainder of the original training ...
Fine-tuning Neural Networks - Deep Learning Dictionary Transfer learning occurs when knowledge that was gained from solving one problem is applied to a new but generally related problem. In the field of deep learning, we can apply transfer learning by using an existing, previously trained network...
A Freytag J Denzler C K¨ading, E Rodner, "Fine-tuning deep neural networks in continuous learning scenarios," ACCV Workshop on Interpretation and Visualization of Deep Neural Nets (ACCV-WS), 2016.Kading, C., Rodner, E., Freytag, A., Denzler, J.: Fine-tuning deep neural networks in...
我们提出的Child-Tuning给出了一种新的解法：在Fine-tuning过程中仅更新预训练模型中部分网络的参数（这部分网络本文就叫做Child Network），这么简单直接的做法却效果奇赞，结果在GLUE上相较标准Fine-tune有0.5～8.6个点的效果提升，但却只需要几行代码的修改，你不想试试吗？目前，该论文《Raise a Child in ...
Learn what is fine tuning and how to fine-tune a language model to improve its performance on your specific task. Know the steps involved and the benefits of using this technique.
Recently, the pretrain-finetuning paradigm has attracted tons of attention in graph learning community due to its power of alleviating the lack of labels problem in many real-world applications. 这句话里,要搞清楚的东西是pretrain-finetuning:它能够解决的问题是缺少label的问题。 Current studies ...
在深度学习中，Fine-tuning和Embedding是两个重要的概念。Fine-tuning是指在预训练模型的基础上，在特定任务上进行进一步训练，以适应该任务的特定数据和要求。而Embedding是一种将高维离散数据转换为低维连续向量表示的技术，常用于将文本、图像等离散数据编码成数值形式，便于深度学习模型处理和学习。通过Fine-tuning，...
Fine-tuning in machine learning is the process of adapting a pre-trained model for specific tasks or use cases through further training on a smaller dataset.
Directly control the overfitting in the neural network L1正则化(Lasso Regularization):通过在损失函数中添加模型权重的绝对值之和作为惩罚项,鼓励模型产生稀疏的权重,即许多权重为零。这有助于模型的解释性,并可能减少过拟合。 L2正则化(Ridge Regularization):通过在损失函数中添加模型权重的平方和作为惩罚项,鼓励...