方法:DRPC(Domain Randomization and Pyramid Consistency领域随机化和金字塔一致性) 创新点:运用CycleGAN方法将原始数据定向风格化(类似于神经风格迁移),同时采用Pyramid Consistency加强图片多尺度的一致性。(CycleGAN方法后续会介绍) First, we propose torandomize the synthetic images with the styles of real images(使...
Different parameterization of the "data reward" function instantiates different manipulation schemes. We showcase data augmentation that learns a text transformation network, and data weighting that dynamically adapts the data sample importance. Experiments show the resulting algorithms significantly improve ...
This process includes crucial steps such as data gathering, data analysis, data manipulation, data visualization, etc. Machine Learning, on the other hand, can be thought of as a sub-field of data science. It also deals with data, but here, we are solely focused on learning how to ...
In this paper, a Data Augmentation with Attention Framework is proposed to address overfitting for robust deepfake detaction. Firstly, we advance the existing Sequential DeepFake Manipulation Architecture by integrating Grad-CAM to focus on critical facial regions, thereby enhancing the interpretive and ...
Learning Data Manipulation for Augmentation and Weighting; Zhiting Hu, Bowen Tan, Ruslan Salakhutdinov, Tom Mitchell, Eric P. Xing; Manipulating data, such as weighting data examples or augmenting with new instances, has been increasingly used to improve model training. Previous work has studied vari...
Machine-learning-based predictive maintenance models, i.e. models that predict breakdowns of machines based on condition information, have a high potential
As was mentioned in Section 2.3.3, we adopted the Shapley additive explanations method for feature importance rankings, as was the approach used in [9]. To further highlight the impact of the VAE-based data augmentation, we also compared the performance of the current state of the art [9]...
preprocessing –e.g., data cleaning, labeling, missing data imputation, and categorical data encoding – as well as data augmentation (including synthetic data generation using generative AI methods) and feature engineering – specifically, automated feature extraction, feature construction and feature ...
Learning Data Manipulation for Augmentation and Weighting; Zhiting Hu, Bowen Tan, Ruslan Salakhutdinov, Tom Mitchell, Eric P. Xing; Manipulating data, such as weighting data examples or augmenting with new instances, has been increasingly used to improve model training. Previous work has studied vari...
Li, P., Li, D., Li, W., Gong, S., Fu, Y., & Hospedales, T. M. (2021). A simple feature augmentation for domain generalization. InProceedings of the IEEE/CVF international conference on computer vision(pp. 8886–8895). Piscataway: IEEE. ...