Towards personalized persuasive dialogue generation for adversarial task oriented dialogue setting

Tiwari, Abhisek; Khandwe, Abhijeet; Saha, Sriparna; Ramnani, Roshni; Maitra, Anutosh; Sengupta, Shamik

doi:10.1016/j.eswa.2022.118775

Cited by 7 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is vital to increase the exploitation of data in order to improve the accuracy of the segmentation of rock slices. Transfer learning is a method that may be used to more effectively handle this issue [17][18][19]. Transfer learning refers to the process of applying the information gained in one area of study or the extracted characteristics to other areas of study that are distinct from one another but are linked.…”

Section: Weakly Supervised Learningmentioning

confidence: 99%

Pore extraction method of rock thin section based on Attention U-Net

Pan

Tao

2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

This paper proposes a solution to the shortcomings of traditional segmentation methods. The labeling method uses the incomplete labeling method in weakly supervised labeling to simplify labeling and combines transfer learning to initialize the weight of the network in advance. According to the above ideas, an end-to-end deep learning model is trained. The fine rock particles have a greater segmentation impact, and in addition to that, when compared with the popular deep learning semantic segmentation approaches, they also have a significant improvement. The next phase is to continue improving the network by optimizing the parameters, with the number of network layers and the total number of parameters remaining unaltered. This requirement must be satisfied before moving on to the next stage. The capability of generalization enhances the impact of segmentation on particles as well as their accuracy. Experiments show that this method is significantly better than the traditional method for segmenting rock flakes with manual operation and has better results in the segmentation and extraction of fine particles compared with the mainstream convolutional neural network.

show abstract

Section: Weakly Supervised Learningmentioning

confidence: 99%