ETM: Effective Tuning Method Based on Multi-Objective and Knowledge Transfer in Image Recognition

With the widespread application of machine learning and deep learning, image recognition has been continuously developed. However, there are still huge challenges in the use of machine learning and deep learning. The tuning processes of algorithms are critical and challenging for their performance. Although there have been many previous works to improve the final accuracy of the recognition algorithms through tuning, these works cannot consider some indicators that are also very important in the actual environment (such as latency, central processing unit (cpu) utilization) in the tuning. In this paper, we propose an effective tuning method based on multi-objective and knowledge transfer, which is solved the above limitations in the image recognition. Specifically, we first use an agent to automatically tune the recognition algorithms, and combine the prediction accuracy and the running latency of each episode as a multi-objective reward signal to guide the update of the internal parameters of the agent. In this way, the agent can continuously select the better algorithm configuration to improve prediction performance. In addition, we improve the efficiency of the above tuning process by transferring knowledge. To do that, we can learn the meta parameters from other small-scale tasks to initialize the agent. In the experiments, we apply the proposed method to tune the eXtreme Gradient Boosting and random forest on 57 image recognition tasks and convolutional neural network on 2 tasks. The experimental results verify that the proposed method achieves average accuracy rankings of 1.92, 1.42 and 1.71 on three algorithms to be optimized, respectively. Especially in terms of latency performance, the proposed method performs best on all the tasks (57 data sets) on the three algorithms to be optimized. In addition, we verify the various components of the proposed method through ablation experiments.