Abstract
Deep learning models are in dire need of training data. This need can be addressed by encouraging data holders to contribute their data for training purpose. Data valuation is a mechanism that assigns a value reflecting a number to each data instances. The SHAP Value is a method for assigning payouts to players of coalition game depending on their contribution to the total payout that entails many criteria for the notion of data value. In this paper, the value of the SHAP parameter is calculated in different convolutional neural network for varieties of image datasets. Calculated SHAP value for each data instance shows whether data is high value or low value and it is different in each model. In other words, if you have an image in the VGG model and it is high value, necessarily, it is not high value in ResNet model. The results show that the value of data varies in each dataset and model. Keywords— Deep learning, SHAP Value.
Recommended Citation
Shobeiri, Seyedamir and Aajami, Mojtaba
(2024)
"Shapley value in convolutional neural networks (CNNs): A Comparative Study,"
American Journal of Science & Engineering (AJSE): Vol. 2:
Iss.
3, Article 4.
Available at:
https://research.smartsociety.org/ajse/vol2/iss3/4