Academic Journal of Computing & Information Science, 2022, 5(12); doi: 10.25236/AJCIS.2022.051202.
Jin Lu1, Xiaoting Wan2
1Guangdong Key Laboratory of Big Data Intelligence for Vocational Education, Shenzhen Polytechnic, Shenzhen, 518055, Guangdong, China
2Guangdong Key Laboratory of Big Data Intelligence for Vocational Education, Shenzhen Polytechnic, Shenzhen, 518055, Guangdong, China
With the development of artificial intelligence technology, the basic judgment of students learning state can be realized through the comprehensive analysis of students face, expression, behavior posture and other multi-modal data.In this paper, we present a new image recognition algorithm based on improved AlexNet and shared parameter transfer learning. The main idea of the proposed method is to apply the technique of shared parameter transfer learning (SPTL) to improve the performance of deep neural network architectures such as AlexNet. Compared with conventional methods, SPTL has several advantages: 1) it can be applied for non-convex optimization problems; 2) it can provide better training speed and robustness; 3) it can achieve high accuracy in both training and testing stages. In addition, our method is easy to implement which it does not require any special prepossessing. After the completely associated layer comes to 10, the acknowledgment rate can be near to 98%.
Affective Computing; Deep Reinforcement Learning; Expression data; Learning Status
Jin Lu, Xiaoting Wan. Image Recognition Algorithm Based on Improved AlexNet and Shared Parameter Transfer Learning. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 12: 6-14. https://doi.org/10.25236/AJCIS.2022.051202.
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