1)Supervised Fine-tuning: Autoregressive 首先第一阶段以自回归形式进行有监督微调,来使得模型适配时序数据,这是因为GPT-2是一个因果语言模型(这里的因果是指以自回归的形式,仅利用历史数据进行预测,并不会采用未来的数据)。这样既保留了语言模型本身的知识,又使模型能够适应时间序列数据。在这个阶段模型输出为patch...
To solve this problem, an object detection network and two-stage fine-tuning approach based on You Only Look Once (YOLO)v4 is proposed in this paper to achieve image recognition of electrical equipment under the condition of small samples. Using the two-stage and dual-network m...
To solve this problem, an object detection network and two-stage fine-tuning approach based on You Only Look Once (YOLO)v4 is proposed in this paper to achieve image recognition of electrical equipment under the condition of small samples. Using the two-stage and dual-network method as the ...
刷到一篇解决分类样本不平衡的论文,通过两阶段训练模型,提高模型对长尾数据的效果。 一阶段:采用class-balanced reweighting loss function训练模型,仅训练BERT的最后一层; 二阶段:采用普通交叉熵正常训练模型。 《Two-Stage Fine-Tuning: A Novel Strategy for Learning ClassImbalanced Data》 ...
The method consists of two stages: pre-training and fine-tuning. The pre-training stage is based on self-supervised contrast learning, which uses unlabeled crack data to learn the potential feature representation in crack images. The parameters after pre-training are loaded in the fine-tuning ...
A two-stage approach is used to build the ML models, GTS-ML from the gun to the sample and STD-ML from the sample to the detector. The BD patterns as the input and the electron beam properties as the data label are applied to train the STD-ML model. To automate the UED instrument...
Proposed approach In this paper, we propose a detector, AccLoc, that combines the advantages of anchor-free detectors and two-stage detectors to extract more effective and sensitive features for accurate object localization. AccLoc mainly consists of a proposal generation module and a proposal refine...
作者:Yang Xu , Shanshan Wu , Biqi Wang , Ming Yang , Zebin Wu , Yazhou Yao , Zhihui Wei 单位:南京理工大学 影响因子:7.5 中科院分区:计算机科学1区 工程技术2区 链接:Two-stage fine-grained image cla…
To optimize the feature extraction module and enhance the final model's performance by supplying initial parameters, we pre-train the network using in the protein-peptide dataset and transfer the network parameters to the PPI site task for fine-tuning in the second stage of transfer learning. (...
It is well known that any search algorithm needs for certain parts to be problem specific. It is very important the way these parts are implemented. A fine tuning of parameters will never balance a bad definition of the solution set, of the neighbourhood or the cost function. In this paper...